Methodology for Implementing the State Estimation in Renewable Energy Management Systems

Abstract

This paper describes a methodology for implementing the state estimation and enhancing the accuracy in large-scale power systems that partially depend on variable renewable energy resources. To determine the actual states of electricity grids, including those of wind and solar power systems, the proposed state estimation method adopts a fast-decoupled weighted least square approach based on the architecture of application common database. Renewable energy modeling is considered on the basis of the point of data acquisition, the type of renewable energy, and the voltage level of the bus-connected renewable energy. Moreover, the proposed algorithm performs accurate bad data processing using inner and outer functions. The inner function is applied to the largest normalized residue method to process the bad data detection, identification and adjustment. While the outer function is analyzed whether the identified bad measurements exceed the condition of Kirchhoff’s current law. In addition, to decrease the topology and measurement errors associated with transformers, a connectivity model is proposed for transformers that use switching devices, and a transformer error processing technique is proposed using a simple heuristic method. To verify the performance of the proposed methodology, we performed comprehensive tests based on a modified IEEE 18-bus test system and a large-scale power system that utilizes renewable energy.

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energies
Article
Methodology for Implementing the State Estimation in
Renewable Energy Management Systems
Yun-Sung Cho and Yun-Hyuk Choi *


Citation: Cho, Y.-S.; Choi, Y.-H.
Methodology for Implementing the
State Estimation in Renewable Energy
Management Systems. Energies 2021,
14, 2301. https://doi.org/10.3390/
en14082301
Academic Editor: Anastasios Dounis
Received: 28 February 2021
Accepted: 13 April 2021
Published: 19 April 2021
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Copyright: © 2021 by the authors.
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Attribution (CC BY) license (https://
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4.0/).
School of Electrical Engineering, Daegu Catholic University, Gyeongbuk 38430, Korea; philos@cu.ac.kr
*Correspondence: yhchoi@cu.ac.kr; Tel./Fax: +82-53-850-2767
Abstract:
This paper describes a methodology for implementing the state estimation and enhancing
the accuracy in large-scale power systems that partially depend on variable renewable energy
resources. To determine the actual states of electricity grids, including those of wind and solar
power systems, the proposed state estimation method adopts a fast-decoupled weighted least square
approach based on the architecture of application common database. Renewable energy modeling
is considered on the basis of the point of data acquisition, the type of renewable energy, and the
voltage level of the bus-connected renewable energy. Moreover, the proposed algorithm performs
accurate bad data processing using inner and outer functions. The inner function is applied to the
largest normalized residue method to process the bad data detection, identification and adjustment.
While the outer function is analyzed whether the identified bad measurements exceed the condition
of Kirchhoff’s current law. In addition, to decrease the topology and measurement errors associated
with transformers, a connectivity model is proposed for transformers that use switching devices, and
a transformer error processing technique is proposed using a simple heuristic method. To verify the
performance of the proposed methodology, we performed comprehensive tests based on a modified
IEEE 18-bus test system and a large-scale power system that utilizes renewable energy.
Keywords:
renewable energy management system; state estimation; bad data processing; renewable
modeling based on telemeter point; tap position estimation
1. Introduction
Recently, Korean electric power systems (KEPS) have been experiencing some cas-
cading outages of generators and photovoltaic (PV) systems. The unusual temperatures
and wrong setting parameters of some protective devices lead to cascading outages. On
March 28 2020, when Shin Boryeong Unit 1 (coal-fired, 805 MW) suddenly stopped, the
grid frequency dropped to 59.8 Hz after 10 s, and the PV system in the grid considered
it a low frequency and stopped. Also, it was found that the frequency further dropped
to
59.67 Hz
[
1
]. About 15.8 GW of the generated solar power from KEPS is lost when
recognizing abnormal frequencies, especially when the frequency drops to 59.3–59.8 Hz.
Korea has recently announced the 9th Basic Plan of Long-Term Electricity Supply and
Demand, which aims at having 78.1 GW of renewable energy by 2034 [
2
]. As shown in
Table 1, the capacity of coal should decrease from 34.7 GW in 2020 to 29.0 GW in 2034. For
the 30 abolished coals, 24 will be converted to liquefied natural gas (LNG). Also, during the
same period, the capacity of LNG facilities should increase from 41.3 to 60.6 GW. Moreover,
the plan aims at maintaining the current gradual reduction in the number of nuclear power
plants and the increase in the number of renewable energy facilities. It is predicted that the
capacity of nuclear power plants will decrease from 24.7 GW in 2019 to 19.4 GW in 2034
and that the capacity of renewable energy facilities will increase from 19.3 to 78.1 GW.
Energies 2021,14, 2301. https://doi.org/10.3390/en14082301 https://www.mdpi.com/journal/energies

Energies 2021,14, 2301 2 of 24
Table 1. The 9th Basic Plan for Long-Term Electricity Supply and Demand.
Source 2019 2020 2030 2034
Renewable 15.8 19.3 57.9 78.1
LNG 39.7 41.3 57.0 60.6
Coal 36.8 34.7 32.6 29.0
Nuclear 23.3 24.7 20.4 19.4
As more variable renewable sources are frequently installed in KEPS, the reliable
assessment and operation of power systems with a high penetration of renewable energy
should depend on network applications and on good-quality data acquisition. Therefore,
one of the most challenging tasks for today’s power system engineers in Korea is the
development and operation of renewable energy management systems (REMSs) to be
used by operators for proper power system operation and planning. In Korea, REMSs
are currently under development, and they usually involve hardware, data acquisition,
databases, applications, and displays, as shown in Figure 1.
Energies 2021, 14, x FOR PEER REVIEW 2 of 24
Table 1. The 9th Basic Plan for Long-Term Electricity Supply and Demand.
Source 2019 2020 2030 2034
Renewable 15.8 19.3 57.9 78.1
LNG 39.7 41.3 57.0 60.6
Coal 36.8 34.7 32.6 29.0
Nuclear 23.3 24.7 20.4 19.4
As more variable renewable sources are frequently installed in KEPS, the reliable as-
sessment and operation of power systems with a high penetration of renewable energy
should depend on network applications and on good-quality data acquisition. Therefore,
one of the most challenging tasks for today’s power system engineers in Korea is the de-
velopment and operation of renewable energy management systems (REMSs) to be used
by operators for proper power system operation and planning. In Korea, REMSs are cur-
rently under development, and they usually involve hardware, data acquisition, data-
bases, applications, and displays, as shown in Figure 1.
Figure 1. Application structure of REMS.
In an REMS, although online data may include incorrect data due to communication
failures or the scale-factor errors of telemetered points, the state estimation is calculated
on the basis of the actual state of a power system using analog and digital data obtained
from supervisory control and data acquisition systems (SCADA) [3,4]. Once a state esti-
mation is carried out, the network’s estimated state should be evaluated in two aspects:
network analysis and economic operation. A dynamic stability assessment is applied to
the calculation of the penetration of renewable energy, and the final solution of the REMS
is determined to control the amount of generated renewable energy based on various ap-
plication outputs, as shown in Figure 1. Then, the security-constrained economic dispatch
uses the state estimation results as inputs and calculates the desired megawatt (MW) out-
put limit for all the units while considering the transmission constraints in normal and
contingency conditions.
The state estimation reliability depends on various factors, such as power system
modeling and the quality of the telemetered data and pseudo measurements. In KEPS,
renewable energy and transformers are among the most important factors in state estima-
tion, as the measurements associated with them are very lacking. In KEPS, the state esti-
mation cannot be calculated up to the exact transformer tap positions because of its unique
Figure 1. Application structure of REMS.
In an REMS, although online data may include incorrect data due to communication
failures or the scale-factor errors of telemetered points, the state estimation is calculated
on the basis of the actual state of a power system using analog and digital data obtained
from supervisory control and data acquisition systems (SCADA) [
3
,
4
]. Once a state esti-
mation is carried out, the network’s estimated state should be evaluated in two aspects:
network analysis and economic operation. A dynamic stability assessment is applied to
the calculation of the penetration of renewable energy, and the final solution of the REMS
is determined to control the amount of generated renewable energy based on various
application outputs, as shown in Figure 1. Then, the security-constrained economic dis-
patch uses the state estimation results as inputs and calculates the desired megawatt (MW)
output limit for all the units while considering the transmission constraints in normal and
contingency conditions.
The state estimation reliability depends on various factors, such as power system
modeling and the quality of the telemetered data and pseudo measurements. In KEPS, re-
newable energy and transformers are among the most important factors in state estimation,
as the measurements associated with them are very lacking. In KEPS, the state estimation
cannot be calculated up to the exact transformer tap positions because of its unique feature

Energies 2021,14, 2301 3 of 24
such as lack of measurement. In order to overcome these problems, a state estimation
technique that adopts a robust tap estimation algorithm and an accurate connectivity model
is required for REMSs.
Many have researched this topic and proposed various techniques. To enhance the
accuracy of state estimation, these research efforts focused on developing three functions.
The first one is enhancing the accuracy of the state estimation using phasor measurement
unit (PMU). Algorithm for estimating the state variables based on a limited number of PMU
as well as determining the optimal PMU placement was proposed [
5
–
7
]. To enhance the
performance of state estimation using various measurements based on the remote terminal
unit (RTU) and PMU, the robust and fast algorithm with the linear weighted least square
(WLS) technique and the architecture based on a multistage scheme is proposed
[8–10].
The second function is identifying the topology and measurement errors of devices using
practical heuristic methods. A state estimation monitoring tool based on pseudo mea-
surements, statistic functions, and coherency checks was proposed, and it could detect
potential topology errors and enhance the performance of state estimation [
11
,
12
]. The
third function is a residual sensitivity method based on the WLS approach, which detects
and replaces bad data points using normalized residuals. The largest normalized residual
method with highly efficient technique was proposed [
13
]. A method for detecting and
identifying topology errors using the recursive Bayesian approach and its improved version
was proposed [
14
]. Also, an orthogonal iteratively re-WLS for solving equality-constrained
state estimations was estimated to power system state variables and transformer tap po-
sitions under erroneous zero-power injections [
15
]. Although it showed good features in
estimating the tap positions of transformers based on both approaches, this approach is
still insufficient of practicality, as there is a lack in comparative studies that are based on
large-scale power systems and in extensive testing trials that use many topology errors.
However, various enhanced algorithms for state estimation were proposed and tested in
small testing and power systems, including non-variable resources.
In this paper, a methodology for implementing the state estimation and enhancing the
accuracy in large-scale power systems that partially depend on variable renewable energy
resources. The methodology implemented in this paper is detailed below:
•
First, the structure of the application common database of renewable energy man-
agement systems containing power system components based on a physical node is
constructed. The application common database is composed of a node-breaker model
and bus-branch model for enhancing the accuracy and speed of network applications.
The aggregated renewable energy is modeled as a generator, transformer and collector
transmission line to estimate the actual system. To overcome the shortage of mea-
surement connected to transformer, the connectivity model of a transformer using a
switching device is proposed.
•
Second, the simple heuristic method based on the condition of feasibility check are
proposed to decrease the effects of the lack of measurements of three winding trans-
former, two winding transformer, and step-up transformer. As the renewable energy
expands, the accuracy of state estimation should be depended on the measurement
associated with transformer connected to renewable energy. The heuristic method is
applied to the topology processing as a preprocessing function.
•
Third, the state estimation based on the fast-decoupled WLS approach is implemented
to estimate the actual state of the power systems including variable renewable energy
resources based on decoupled gain matrix, pseudo measurement and bad data process-
ing. The bad data processing is composed of an inner processing module and outer
processing module. The inner processing module based on the largest normalized
residue method deals with the bad data detection, identification, and adjustment. The
tap position was estimated through a modified sensitivity calculation for reactive flow
and voltage measurements. The outer processing module performs the function of
analyzing that the bad measurement selected in inner processing exceeds the condition

Energies 2021,14, 2301 4 of 24
of Kirchhoff’s current law. The outer processing method is applied to the bad data
processing as a postprocessing function
•Finally, the performance of the proposed methodology is validated through the com-
prehensive tests based on a modified IEEE 18-bus test system and a large-scale power
system that utilizes renewable energy. The performance for large-scale power system is
validated through the dynamic test for assessing the performance requirements based
on ERCOT, analyzing the system at different meter accuracy range, and performing
the pseudo measurement processing and bad data processing for severe events.
2. Structure and Model of REMSs
2.1. REMS Structure
In the considered REMS in this study, the database consists of a real-time database
(RTDB), an application common database (ACDB), and an offline database (OFFDB), as
shown in Figure 1. The RTDB handles the SCADA information and the remote terminal unit
(RTU), and the ACDB handles the state estimation I/O, the power flow analysis, and other
applications [
16
]. The OFFDB handles all the data acquisition information, the application,
and the display. The topology processing module in state estimation periodically transfers
online data from the RTDB to the ACDB. If the OFFDB is updated on the basis of a common
information model (CIM), the RTDB and ACDB are also updated and maintained. The
ACDB is composed of static and network hierarchy model data in addition to dynamic data
for the hierarchy model. Each block in Figure 2represents a static table and a dynamic table
for a system hierarchic layer, which is composed of several fields. The link list method was
used to create a relationship between the tables. Each table of the database has relationships
using one or more of the following three link types.
Energies 2021, 14, x FOR PEER REVIEW 4 of 24
the condition of Kirchhoff’s current law. The outer processing method is applied to
the bad data processing as a postprocessing function
• Finally, the performance of the proposed methodology is validated through the com-
prehensive tests based on a modified IEEE 18-bus test system and a large-scale power
system that utilizes renewable energy. The performance for large-scale power system
is validated through the dynamic test for assessing the performance requirements
based on ERCOT, analyzing the system at different meter accuracy range, and per-
forming the pseudo measurement processing and bad data processing for severe
events.
2. Structure and Model of REMSs
2.1. REMS Structure
In the considered REMS in this study, the database consists of a real-time database
(RTDB), an application common database (ACDB), and an offline database (OFFDB), as
shown in Figure 1. The RTDB handles the SCADA information and the remote terminal
unit (RTU), and the ACDB handles the state estimation I/O, the power flow analysis, and
other applications [16]. The OFFDB handles all the data acquisition information, the ap-
plication, and the display. The topology processing module in state estimation periodi-
cally transfers online data from the RTDB to the ACDB. If the OFFDB is updated on the
basis of a common information model (CIM), the RTDB and ACDB are also updated and
maintained. The ACDB is composed of static and network hierarchy model data in addi-
tion to dynamic data for the hierarchy model. Each block in Figure 2 represents a static
table and a dynamic table for a system hierarchic layer, which is composed of several
fields. The link list method was used to create a relationship between the tables. Each table
of the database has relationships using one or more of the following three link types.
Figure 2. State estimation database.
The network analysis role in the REMS is to analyze the actual power system static
analysis state and to calculate the maximum renewable energy penetration using a power
flow analysis, a contingency analysis, voltage stability, and transient stability. As shown
in Figure 3, the major parts of the applications in the REMS are composed of a SCADA
Figure 2. State estimation database.
The network analysis role in the REMS is to analyze the actual power system static
analysis state and to calculate the maximum renewable energy penetration using a power
flow analysis, a contingency analysis, voltage stability, and transient stability. As shown in
Figure 3, the major parts of the applications in the REMS are composed of a SCADA level
w.r.t monitoring, an ON-LINE level w.r.t network analysis, and an OFF-LINE level w.r.t a
further study using offline software. The analog data of voltage, tap position, active power,

Energies 2021,14, 2301 5 of 24
and reactive power is acquired as 2 s and the digital data of the status of circuit breaker is
acquired as 4 s. The total number of analog and digital data is 128,809. State estimation
plays a key role in the calculation of the maximum renewable energy penetration using
online dynamic stability assessment systems. The reliability of most of the applications
is based on a power flow technique that depends on the state estimation solution quality.
State estimation and powerflow run 1 min, and steady-state assessment runs 2 min, and
dynamic stability assessment runs 5 min.
Energies 2021, 14, x FOR PEER REVIEW 5 of 24
level w.r.t monitoring, an ON-LINE level w.r.t network analysis, and an OFF-LINE level
w.r.t a further study using offline software. The analog data of voltage, tap position, active
power, and reactive power is acquired as 2 s and the digital data of the status of circuit
breaker is acquired as 4 s. The total number of analog and digital data is 128,809. State
estimation plays a key role in the calculation of the maximum renewable energy penetra-
tion using online dynamic stability assessment systems. The reliability of most of the ap-
plications is based on a power flow technique that depends on the state estimation solu-
tion quality. State estimation and powerflow run 1 min, and steady-state assessment runs
2 min, and dynamic stability assessment runs 5 min.
Network
Topology Processing
State Estimation
Powerflow
Data Handling
- Netwokr/Dynamic
- Contingency/Scenario
- Transfer analysis/etc
Steady-State
Assessment
Data Conversion
(PSS/E Data Format)
STUDY LEVEL
ON-LINE LEVEL
Use Commercial
Package
Further Study?
PSS/E
DSAT
Yes
Real-Time
DB
direct access key
Analogy/digital data
SCADA
Topology Processing
(operating time: 2 seconds)
direct access
key
analogy/digital
Trigger
signal
SCADA LEVEL
Power system
Monitoring
Generation/Load/Etc
Various Information
Assessment of
Actual State
- Basecase/Contingency
- Trans./Voltage stability
Calculation of
Renewable limit
- Transfer analysis
- Trans./Voltage stability
Dynamic Stability Assessment
Figure 3. Network analysis structure in the REMS.
2.2. Renewable Modeling
As shown in Figure 4, the telemetry information for renewable energy connected
to >154- and 22.9-kV dedicated lines was acquired in the REMS. On the basis of the telem-
etered points of SCADA and RTU, the renewable energy sources, such as the solar PV and
wind power plant, were modeled as generators in the network application. The procedure
of modeling the renewable energy plants and the interface between the renewable energy
and telemetered point is described as follows:
Figure 4. Locations of the analog points in KEPS.
Figure 3. Network analysis structure in the REMS.
2.2. Renewable Modeling
As shown in Figure 4, the telemetry information for renewable energy connected to
>154- and 22.9-kV dedicated lines was acquired in the REMS. On the basis of the telemetered
points of SCADA and RTU, the renewable energy sources, such as the solar PV and wind
power plant, were modeled as generators in the network application. The procedure of
modeling the renewable energy plants and the interface between the renewable energy
and telemetered point is described as follows:
Energies 2021, 14, x FOR PEER REVIEW 5 of 24
level w.r.t monitoring, an ON-LINE level w.r.t network analysis, and an OFF-LINE level
w.r.t a further study using offline software. The analog data of voltage, tap position, active
power, and reactive power is acquired as 2 s and the digital data of the status of circuit
breaker is acquired as 4 s. The total number of analog and digital data is 128,809. State
estimation plays a key role in the calculation of the maximum renewable energy penetra-
tion using online dynamic stability assessment systems. The reliability of most of the ap-
plications is based on a power flow technique that depends on the state estimation solu-
tion quality. State estimation and powerflow run 1 min, and steady-state assessment runs
2 min, and dynamic stability assessment runs 5 min.
Network
Topology Processing
State Estimation
Powerflow
Data Handling
- Netwokr/Dynamic
- Contingency/Scenario
- Transfer analysis/etc
Steady-State
Assessment
Data Conversion
(PSS/E Data Format)
STUDY LEVEL
ON-LINE LEVEL
Use Commercial
Package
Further Study?
PSS/E
DSAT
Yes
Real-Time
DB
direct access key
Analogy/digital data
SCADA
Topology Processing
(operating time: 2 seconds)
direct access
key
analogy/digital
Trigger
signal
SCADA LEVEL
Power system
Monitoring
Generation/Load/Etc
Various Information
Assessment of
Actual State
- Basecase/Contingency
- Trans./Voltage stability
Calculation of
Renewable limit
- Transfer analysis
- Trans./Voltage stability
Dynamic Stability Assessment
Figure 3. Network analysis structure in the REMS.
2.2. Renewable Modeling
As shown in Figure 4, the telemetry information for renewable energy connected
to >154- and 22.9-kV dedicated lines was acquired in the REMS. On the basis of the telem-
etered points of SCADA and RTU, the renewable energy sources, such as the solar PV and
wind power plant, were modeled as generators in the network application. The procedure
of modeling the renewable energy plants and the interface between the renewable energy
and telemetered point is described as follows:
Figure 4. Locations of the analog points in KEPS.
Figure 4. Locations of the analog points in KEPS.
(1)
Model 1: For the renewable energy connected to >154 kV, it was modeled based on a
generator, a step-up transformer, and a transmission line.

Energies 2021,14, 2301 6 of 24
(2)
Model 2: For the renewable energy connected to the 22.9 kV dedicated line, it was
modeled on the basis of a generator and a step-up transformer.
(3)
Models 1 and 2: The renewable energy generation was calculated using the acquired
data because the losses in the transformers were very small. More detailed data are
shown in Table A1 [17,18].
3. Renewable State Estimation
3.1. Topology Error Model Associated with Transformer
In KEPS, transformers are among the critical parameters in state estimation because
the active power (MW) and reactive power (MVAR) of the secondary winding and the tap
position of the primary winding are only obtained for substations. Also, the telemetered
data associated with step-up transformers in power stations do not exist. Recently, the
tap positions of unattended substations were measured, and the measured values were
included in bad measurements. As shown in Figure 4, the lack of measurement data
associated with transformers as well as the use of suspected data may cause an observability
problem as well as inaccurate state estimation results. Because of these problems, the
tap estimation function was not well operated in KEPS. To get a precise expression for
improving the accuracy of state estimation, a connectivity model of a three-winding
transformer using switching devices and common nodes is proposed, as shown in Figure 5.
Energies 2021, 14, x FOR PEER REVIEW 6 of 24
(1) Model 1: For the renewable energy connected to >154 kV, it was modeled based on a
generator, a step-up transformer, and a transmission line.
(2) Model 2: For the renewable energy connected to the 22.9 kV dedicated line, it was
modeled on the basis of a generator and a step-up transformer.
(3) Models 1 and 2: The renewable energy generation was calculated using the acquired
data because the losses in the transformers were very small. More detailed data are
shown in Table A1 [17,18].
3. Renewable State Estimation
3.1. Topology Error Model Associated with Transformer
In KEPS, transformers are among the critical parameters in state estimation because
the active power (MW) and reactive power (MVAR) of the secondary winding and the tap
position of the primary winding are only obtained for substations. Also, the telemetered
data associated with step-up transformers in power stations do not exist. Recently, the tap
positions of unattended substations were measured, and the measured values were in-
cluded in bad measurements. As shown in Figure 4, the lack of measurement data associ-
ated with transformers as well as the use of suspected data may cause an observability
problem as well as inaccurate state estimation results. Because of these problems, the tap
estimation function was not well operated in KEPS. To get a precise expression for im-
proving the accuracy of state estimation, a connectivity model of a three-winding trans-
former using switching devices and common nodes is proposed, as shown in Figure 5.
Figure 5. Connectivity model of three-winding transformer [19].
The three-winding transformer is modeled using two-winding transformers con-
nected together at a common bus that has no physical meaning. Three circuit breakers
(CBs) are connected at the common bus, which controls the in or out of service status of
the transformer. As shown in Figure 6, the procedure of analyzing the three-winding
transformer is described as follows:
(1) Step 1: Add the TRCB #1, #2, and # 3 w.r.t the three-winding transformer connected
to the common node (ND) if topology processing runs firstly.
(2) Step 2: Create a dynamic link between the common ND of the three-winding trans-
former and the CB. Some links between the three-winding transformer and the ND
as well as between the CB and the three-winding transformer should be added.
(3) Step 3: Perform the feasibility check based on the CB’s state and measurements of P,
Q and Tap associated with transformer as shown in Table 2.
(4) Step 4: Assign the status of the new CB based on the state of the equipment connected
to the three-winding transformer based on the conditions of feasibility check.
(5) Step 5: Perform a topology processing and state estimation to prevent the MVAR
flow toward the open winding of the transformer
Figure 5. Connectivity model of three-winding transformer [19].
The three-winding transformer is modeled using two-winding transformers connected
together at a common bus that has no physical meaning. Three circuit breakers (CBs) are
connected at the common bus, which controls the in or out of service status of the trans-
former. As shown in Figure 6, the procedure of analyzing the three-winding transformer is
described as follows:
(1)
Step 1: Add the TRCB #1, #2, and # 3 w.r.t the three-winding transformer connected
to the common node (ND) if topology processing runs firstly.
(2)
Step 2: Create a dynamic link between the common ND of the three-winding trans-
former and the CB. Some links between the three-winding transformer and the ND as
well as between the CB and the three-winding transformer should be added.
(3)
Step 3: Perform the feasibility check based on the CB’s state and measurements of P,
Q and Tap associated with transformer as shown in Table 2.
(4) Step 4: Assign the status of the new CB based on the state of the equipment connected
to the three-winding transformer based on the conditions of feasibility check.
(5) Step 5: Perform a topology processing and state estimation to prevent the MVAR flow
toward the open winding of the transformer

Energies 2021,14, 2301 7 of 24
Table 2. Conditions of feasibility check for three winding transformer.
CASE CB’s State Measurement TR State
1 Close >Threshold In-service
2 Close <Threshold Out-of-service
3 Open >Threshold Out-of-service
4 Open <Threshold Out-of-service
5 Previous SE Close Previous SE > Threshold In-service
Energies 2021, 14, x FOR PEER REVIEW 7 of 24
Table 2. Conditions of feasibility check for three winding transformer.
CASE CB’s State Measurement TR State
1 Close >Threshold In-service
2 Close <Threshold Out-of-service
3 Open >Threshold Out-of-service
4 Open <Threshold Out-of-service
5 Previous SE Close Previous SE > Threshold In-service
Figure 6. Flowchart of the topology error processing associated with transformer.
Through the proposed approach, the operating states of the three-winding trans-
former were dynamically determined by handling the status of the switching devices
without additional OFFDB. From the database of the EMS perspective, the new links
among the CB, ND, and three-winding transformer were created at the step of the OFFDB,
which is based on the CIM and is stored in an Oracle relational database management
system. If the proposed scheme w.r.t the three-winding transformer was processed in an
OFFDB, several functions, such as a modification of the three-winding transformer in the
CIM, would have been modified and validated. Importantly, the OFFDB validation was
among the critical factors in the EMS. However, these approaches are very complex.
3.2. State Estimation Methodology
The state estimation algorithm is based on a fast-decoupled WLS technique, which
uses a decoupled right-hand side and a constant decoupled gain matrix computed at a flat
voltage. The state estimation can be mathematically formulated as in the following prob-
lem [20–22]:
𝑀𝑖𝑛
𝐽
(𝑥,…,𝑥)=[𝑧 −
𝑓
(𝑥,…,𝑥)]
𝜎

 (1)
Figure 6. Flowchart of the topology error processing associated with transformer.
Through the proposed approach, the operating states of the three-winding transformer
were dynamically determined by handling the status of the switching devices without
additional OFFDB. From the database of the EMS perspective, the new links among the
CB, ND, and three-winding transformer were created at the step of the OFFDB, which is
based on the CIM and is stored in an Oracle relational database management system. If the
proposed scheme w.r.t the three-winding transformer was processed in an OFFDB, several
functions, such as a modification of the three-winding transformer in the CIM, would have
been modified and validated. Importantly, the OFFDB validation was among the critical
factors in the EMS. However, these approaches are very complex.
3.2. State Estimation Methodology
The state estimation algorithm is based on a fast-decoupled WLS technique, which
uses a decoupled right-hand side and a constant decoupled gain matrix computed at a
flat voltage. The state estimation can be mathematically formulated as in the following
problem [20–22]:
Min J(x1, . . . , xNs)=
Nm
∑
i=1
zmeas
i−fi(x1, . . . , xNs)2
σ2
i
(1)

Energies 2021,14, 2301 8 of 24
where f
i
= a function used to calculate the value measured using the i
th
measurement;
σi2
= variance for the ith measurement; J(x) = measurement residual; N
m
= number of
independent measurements; z
imeas
= ith measured quantity; N
s
= number of unknown
parameters.
Figure 7shows the overall flow chart of the state estimation adopting the proposed
algorithm for the fast WLS approach and bad data processing.
Energies 2021, 14, x FOR PEER REVIEW 8 of 24
where fi = a function used to calculate the value measured using the ith measurement; σi2 =
variance for the ith measurement; J(x) = measurement residual; Nm = number of independ-
ent measurements; zimeas = ith measured quantity; Ns = number of unknown parameters.
Figure 7 shows the overall flow chart of the state estimation adopting the proposed
algorithm for the fast WLS approach and bad data processing.
Figure 7. State estimation structure.
To solve the value of G∙Δx = B, forward and backward substitutions using the factor-
ized gain matrix should be performed. The fast-decoupled WLS method uses a fixed gain
matrix. This approach calculates two gain matrices for the voltage angle and magnitude.
The gain matrix assigns in-service buses to the rows of the gain matrix. Depending on the
type of measurement, off-diagonal entries are created. Two gain matrices are created: the
MW-angle and MVAR-magnitude. The structure of the two matrices is the same. To im-
prove the accuracy, the high-voltage direct current (HVDC) system and flexible AC trans-
mission system (FACTS) are modeled. For the HVDC system, the direct current (DC) is
defined as a state variable to be estimated in the MW-angle iteration [23]. Vd and X denote
the DC voltage and the reactance of the DC line, respectively.
𝑉=3√2/𝜋⋅𝑉 ⋅𝑐𝑜𝑠𝛼−3/𝜋⋅𝑋⋅𝐼
 (2)
The FACTSoutput corresponding to its terminal voltage is calculated using Equation (3).
If the FACTSoutput will be outside limits, the change of MVAR is calculated and voltage
magnitude is updated. FACTSvalue denote the last estimated value, and FACTSvalue denote
the last estimated value.
𝐹𝐴𝐶𝑇𝑆 =(𝑉 −𝑉
)∗𝐹𝐴𝐶𝑇𝑆
𝐹𝐴𝐶𝑇𝑆 =(𝐹𝐴𝐶𝑇𝑆
 −𝐹𝐴𝐶𝑇𝑆)×𝐹𝐴𝐶𝑇𝑆 (3)
Figure 7. State estimation structure.
To solve the value of G·∆x=B, forward and backward substitutions using the factor-
ized gain matrix should be performed. The fast-decoupled WLS method uses a fixed gain
matrix. This approach calculates two gain matrices for the voltage angle and magnitude.
The gain matrix assigns in-service buses to the rows of the gain matrix. Depending on
the type of measurement, off-diagonal entries are created. Two gain matrices are created:
the MW-angle and MVAR-magnitude. The structure of the two matrices is the same. To
improve the accuracy, the high-voltage direct current (HVDC) system and flexible AC
transmission system (FACTS) are modeled. For the HVDC system, the direct current (DC)
is defined as a state variable to be estimated in the MW-angle iteration [
23
]. V
d
and X
denote the DC voltage and the reactance of the DC line, respectively.
Vd=3√2/π·Vac ·cos α−3/π·X·Id(2)
The FACTS
output
corresponding to its terminal voltage is calculated using Equation (3).
If the FACTS
output
will be outside limits, the change of MVAR is calculated and voltage
magnitude is updated. FACTS
value
denote the last estimated value, and FACTS
value
denote
the last estimated value.
FACTSout put =(Vmeasurement −Vestimated)∗FACTSslop
FACTSchange =FACTSout put −FACTSvalue×FACTSsensivitity (3)

Energies 2021,14, 2301 9 of 24
3.3. Inner Bad Data Processing
The bad data processing of the MW-angle iteration handles the MW measurements,
DC measurements, and phase shift taps, whereas the bad data processing of the MVAR-
magnitude iteration handles the MVAR and voltage. There is a simple methodology for
computing the bad data processing, and it is illustrated as follows:
(1)
Identify the measurement with the highest normalized residue (r
Ni
) and check if its
normalized residue is above a pre-specified limit.
rNi =∆Zi
σi√Πi
(4)
where
Πi
is the ith diagonal of the residual sensitivity matrix W. The sensitivity matrix
can be written as [20–22]
W=I−HHtR−1H−1HtR−1(5)
(2)
Perform the outer bad data processing and confirm the bad measurement.
(3)
Calculate the replacement of the identified bad measurement. The replacement is
expressed as
Zk(new)=Zk(old)+∆Zk(6)
∆Zk=
m
∑
i=1
∧
∆Zi·∆Zi
σ2/
m
∑
i=1
∧
(∆Zi)2
σ2(7)
where mis the number of measurements.
(4)
Adjust the calculated voltage magnitudes and angles and the other measurements
residue based on the bad measurement replacement.
(5)
Go back to Step 1 until all the bad measurements are processed. If the number of
iterations w.r.t the bad data processing is more than the threshold value, skip the bad
data processing step.
3.4. Outer Bad Data Processing
The outer function is performed to analyze the influence of the identified bad mea-
surements. If the measurements exceed the condition of Kirchhoff’s current law (KCL),
the weighting factor of the measurements is decreased. The condition of KCL is described
as follows:
(1) If the measurement is related with the results of topology error model associated with
transformer, the measurement is selected.
(2) If the accumulated standard deviation and bias of the measurement using
Equation (8)
has high value, the measurement is selected.
STDnew =qSTD2
old ×runcount +residue2/(1+runcount)
Biasnew =(Bias ×runcount +residue)/(1+runcount )(8)
3.5. Tap Position Estimation
As shown in Table 3, the metering point of the transformer is less than that of the
other devices, such as the transmission line, generator, shunt, and HVDC. In KEPS, as the
grid connection of the renewable energy increases, the telemeter point associated with
the transformer affects the state estimation accuracy. To enhance the accuracy of the tap
position estimation of the two- and three-winding transformers, the modified residues
of the MVAR flow and voltage at one terminal of the transformer are used to decide if a
magnitude tap adjustment is required. The tap is adjusted until the measurement residue
is minimized. The tap position estimation is adopted to the bad data processing for the

Energies 2021,14, 2301 10 of 24
MVAR-magnitude iteration. The proposed method of the transformer tap position is
as follows:
(1)
Assign the active power of the transmission line and transformer to the active power
of the renewable energy generator, as shown in Figure 4. The weighting factor of the
renewable energy generator is set as pseudo.
(2) Calculate the bus mismatch of the secondary side of the transformer in the substation.
If the mismatch is rather a threshold value, the measurement connected to the bus is
regarded as a suspect flag.
(3)
Perform a tap position estimation if the state estimation is partially converged. Calcu-
late the sensitivities of the MVAR flow and bus voltage to the measurements of each
transformer based on Equation (9). Then, compute the k
th
column of H
t
R
−1
in the
sensitivity matrix. Afterward, perform a forward/backward substitution with the
gain matrix and multiple H and substitute one.
Tapknew =Tapkold +∆Adjk
∵∆Adjk=n∑m
j=1(∆Sj·∆Zj)/σ2oMvar +n∑n
j=1(∆Sj·∆Zj)/σ2ovolt age
n∑m
j=1∆Sj2/σ2oMvar +n∑n
j=1∆Sj2/σ2ovolt age
(9)
where mand ndenote the number of measurements associated with the MVAR flows
of the branch and injection and the bus voltages for each transformer, respectively.
(4) Adjust the transformer tap based on Equation (9) if the adjustment value is more than
the threshold value.
(5) Go back to Step 3 until all the transformers with an on-load tap changer are processed.
Table 3. Measurements related to the transformer in Korea.
TR Type S/S Type Winding Type Tap Adjust. Metering Point of Pri. Winding Metering Point of Sec. Winding
P1Q1V1Tap P2Q2V2
I GEN 2TR X X X O X X X O
II 765 kV 3TR O O O O O O O O
III 345 kV 3TR O X X O O O O O
IV 154 kV 2TR O X X O O O O X
O: Telemetered point, X: Non-telemetered point, Gen: Power plant.
4. Case Study
A case study was described to demonstrate the performance of the state estimation
method using two kinds of extensive simulations. In the first simulation, static testing for
the individual function in the state estimation was applied using a modified IEEE 18-bus
test system. The test system was constructed by adding renewable energy to the IEEE
14-bus test system [
24
]. Figure 8shows the configuration of the test system considering
the characteristics of the measurement locations in KEPS. The number of all the analog
measurements in this test system was 125. The number of the installed analog measurement
devices of the MW, MVAR, voltage, and tap position was 50, 51, 13, and 2 respectively. The
total number of analog measurements and their ratio were 115 and 92%, respectively. In
the second simulation, a comprehensive dynamic test of the state estimation adopting the
proposed algorithm was performed in large-scale power systems.

Energies 2021,14, 2301 11 of 24
Energies 2021, 14, x FOR PEER REVIEW 11 of 24
file. The modified IEEE 18-bus test system was decomposed into different CSV files based
on the ACDB structure. To demonstrate the performance of the proposed algorithm, var-
ious scenarios were used, as shown in Table 4.
Figure 8. Modified IEEE 18-bus test system including renewable energy.
Table 4. Scenario for validating the state estimation algorithm.
Scenario ID Validation Function Description
A Estimation
- Estimate the voltage and flow
- Range of suspect ratio* between 0% and 20%
B Bad data processing
- Identification and replacement of bad data
- Good data with wrong value
C Tap position estimation
- Identification and replacement of tap position
- Good data with wrong value
- Suspect data for tap position and Mw/Mvar
D Comparative study
- Compare the results of the proposed algorithm
with existing method
*ratio = 100× (suspected measurement/all measurements). The measurement has a quality code,
such as good and suspect.
4.1.1. Scenario A
In order to validate the accuracy and reliability of the proposed state estimation
method in REMS, a comparative simulation between an REMS and measurements with
some suspects was performed at various conditions. In the SCADA and EMS, the quality
flag of the telemetered data was set to a suspect when a communication failure occurs.
Tables 5–7 show the comparative results of the state estimation for various cases. Cases
1–3 were applied to different suspect ratios: 0%, 10%, and 20%, respectively. The voltage,
generation, and branch flow for the three cases were compared with the true values. The
Figure 8. Modified IEEE 18-bus test system including renewable energy.
4.1. Static Tests
The proposed algorithm for the state estimation was evaluated by applying input data,
where the electrical information of the digital/analog data, power system components,
and user-defined parameters were constructed in a comma-separated values (CSV) file.
The modified IEEE 18-bus test system was decomposed into different CSV files based on
the ACDB structure. To demonstrate the performance of the proposed algorithm, various
scenarios were used, as shown in Table 4.
Table 4. Scenario for validating the state estimation algorithm.
Scenario ID Validation Function Description
AEstimation - Estimate the voltage and flow
- Range of suspect ratio * between 0% and 20%
BBad data processing - Identification and replacement of bad data
- Good data with wrong value
CTap position estimation
- Identification and replacement of tap position
- Good data with wrong value
- Suspect data for tap position and Mw/Mvar
D Comparative study - Compare the results of the proposed
algorithm with existing method
* ratio = 100
×
(suspected measurement/all measurements). The measurement has a quality code, such as good
and suspect.
4.1.1. Scenario A
In order to validate the accuracy and reliability of the proposed state estimation
method in REMS, a comparative simulation between an REMS and measurements with
some suspects was performed at various conditions. In the SCADA and EMS, the quality

Energies 2021,14, 2301 12 of 24
flag of the telemetered data was set to a suspect when a communication failure occurs.
Tables 5–7show the comparative results of the state estimation for various cases. Cases
1–3 were applied to different suspect ratios: 0%, 10%, and 20%, respectively. The voltage,
generation, and branch flow for the three cases were compared with the true values. The
results for Case 1 exhibited good agreement with the REMS and the measurement results.
As for Cases 2 and 3, the suspect measurements of the MW, MVAR, and voltage were
randomly selected. On the basis of these results, the estimated values of the test system
can be accurately calculated from the measurements with the suspect quality flag.
Table 5. Estimated voltage magnitude and angle for Scenario A.
Bus
Num.
True Value Case 1: Ratio * 0% Case 2: Ratio * 10% Case 3: Ratio * 20%
Mag. Ang. Mag. Ang. Mag. Ang. Mag. Ang.
1 1.065 0.00 1.065 0.00 1.065 0.00 1.065 0.00
2 1.045 −3.52 1.045 −3.52 1.045 −3.52 1.046 −3.42
3 1.012 −10.59 1.012 −10.59 1.012 −10.59 1.011 −10.75
4 0.986 −8.96 0.986 −8.96 0.986 −8.97 0.987 −9.22
5 0.998 −7.26 0.998 −7.26 0.998 −7.26 0.998 −7.44
6 0.942 −12.56 0.942 −12.56 0.942 −12.56 0.940 −12.81
7 0.976 −10.05 0.976 −10.05 0.976 −10.05 0.979 −10.29
8 0.979 −10.05 0.979 −10.05 0.979 −10.05 0.982 −10.28
9 0.979 −9.74 0.979 −9.74 0.979 −9.74 0.981 −9.98
10 0.944 −14.22 0.944 −14.22 0.944 −14.22 0.942 −14.40
11 0.934 −14.21 0.934 −14.21 0.934 −14.21 0.932 −14.42
12 0.967 −10.04 0.967 −10.05 0.967 −10.04 0.966 −10.33
13 0.905 −13.52 0.905 −13.52 0.905 −13.52 0.903 −13.77
14 0.946 −9.17 0.946 −9.17 0.946 −9.18 0.943 −9.31
15 1.000 −1.81 0.999 −1.82 0.999 −1.84 0.995 −1.86
16 1.000 −0.61 1.000 −0.61 0.999 −0.64 0.995 −0.65
17 1.010 −9.92 1.010 −9.92 1.010 −9.92 1.009 −10.10
18 0.970 −8.21 0.970 −8.22 0.970 −8.21 0.969 −8.50
* Suspect ratio (15/115 = 10%, 30/115 = 20%), Mag. (pu)/Ang. (degree).
Table 6. Estimated generation for Scenario A.
IJ.
Num.
True Value Case 1: Ratio 0% Case 2: Ratio 10% Case 3: Ratio 20%
MW MVAR MW MVAR MW MVAR MW MVAR
Gen1 181.2 15.7 181.2 15.7 181.2 15.7 179.1 15.1
Gen2 80.0 47.1 80.0 47.1 80.0 47.1 81.8 46.3
Gen3 35.0 1.2 35.0 1.2 34.8 1.0 34.9 0.3
Gen4 20.0 −3.1 20.0 −3.1 20.0 −3.1 19.2 −2.9
Gen5 50.0 5.8 50.0 5.9 50.0 5.9 49.9 5.6
Table 7. Estimated branch flow for Scenario A.
BR.
Num.
True Value Case 1: Ratio 0% Case 2: Ratio 10% Case 3: Ratio 20%
MW MVAR MW MVAR MW MVAR MW MVAR
LN1 116.1 −1.5 116.1 −1.5 116.1 −1.5 112.6 −1.8
LN3 67.0 3.2 67.0 3.2 67.0 3.2 69.6 3.8
LN11 41.4 13.4 41.4 13.4 41.4 13.4 41.2 15.2
LN14 27.3 0.7 27.3 0.7 27.3 0.7 27.3 1.1
TR1 26.3 18.3 26.3 18.4 26.3 18.3 26.6 18.9
TR2 72.8 39.7 72.8 39.7 72.8 39.6 71.7 33.4
4.1.2. Scenario B
This scenario illustrates the bad data detection and replacement function of the state
estimation. The simulation condition of this case is identical to Case 1 of scenario A, except

Energies 2021,14, 2301 13 of 24
for the wrong value with the good-quality flag, which could be generated by the scale-factor
error of the measurement units as well as by human error w.r.t the RTDB maintenance.
Table 8shows the estimated values of the various variables for the voltage, MW, and MVAR,
which were used to investigate the bad data processing of the REMS. I
†
represents the
wrong input data of bad data processing. Table 9shows the normalized residuals and
sensitivity of the bad data detection and replacement for the four cases. Even though the
input data had a big error, the results exhibited good agreement with the REMS and the
true values as shown in Figure 9.
Table 8. Results of the bad data processing for Scenario B.
#S/S W.ID * Meas.
Type
True
Value
Case 4 Case 5 Case 6 Case 7
I†Est. I †Est. I †Est. I †Est.
1
a Mw 80 79.7 80 80 77.8
b Mvar 12.7 12.7 12.8 20 15.5 12.3
c Volt 160.9 160.9 152 160.9 152 160.8 160.9
d Mw −
113.8
−90 −
113.8
−
113.8
−
113.8
−
113.2
e Mw 44.0 43.9 43.9 43.9 43.9
f Mw 61.1 40 61.0 61.1 61.1 60.9
g Mvar 3.2 30 3.1 2.68 20 2.3 4.5
2
h Mw 94.2 93.9 94.2 94.2 75 83.5
i volt 155.8 155.9 150 156.0 156.0 155.5
j Mw −65.1 −40 −64.8 −65.1 −65.1 −45 −61.8
k Mw −20 −20.1 −20 −20 −40 −15.3
m Mvar 18.3 18.4 18.9 18.7 16.4
* Wrong measurement ID, †Input value of the wrong measurement.
Table 9. Normalized residuals and sensitivity for the detection and replacement of bad data.
Case W.ID Normalized
Residual †
Sensitivity
Value †† Case W.ID Normalized
Residual †
Sensitivity
Value ††
4 d 2.98 −0.239 6 b 1.17 - *
f 3.44 0.209 c 2.85 0.057
g 4.58 −0.275 g 2.806 −0.177
j 3.72 −0.233 7 h 3.42 −0.235
5 c 2.88 0.058 j 5.02 −0.304
i 1.0 - * k 2.01 - *
* Normalized residual below a threshold of 2.5, †Equation (4), †† Equation (7).
Energies 2021, 14, x FOR PEER REVIEW 13 of 24
for the wrong value with the good-quality flag, which could be generated by the scale-
factor error of the measurement units as well as by human error w.r.t the RTDB mainte-
nance. Table 8 shows the estimated values of the various variables for the voltage, MW,
and MVAR, which were used to investigate the bad data processing of the REMS. I
†
rep-
resents the wrong input data of bad data processing. Table 9 shows the normalized resid-
uals and sensitivity of the bad data detection and replacement for the four cases. Even
though the input data had a big error, the results exhibited good agreement with the
REMS and the true values as shown in Figure 9.
Table 8. Results of the bad data processing for Scenario B.
#S/S W.ID *
Meas.
Type
True
Value
Case 4 Case 5 Case 6 Case 7
I
†
Est. I
†
Est. I
†
Est. I
†
Est.
1
a Mw 80 79.7 80 80 77.8
b Mvar 12.7 12.7 12.8 20 15.5 12.3
c Volt 160.9 160.9 152 160.9 152 160.8 160.9
d Mw −113.8 −90 −113.8 −113.8 −113.8 −113.2
e Mw 44.0 43.9 43.9 43.9 43.9
f Mw 61.1 40 61.0 61.1 61.1 60.9
g Mvar 3.2 30 3.1 2.68 20 2.3 4.5
2
h Mw 94.2 93.9 94.2 94.2 75 83.5
i volt 155.8 155.9 150 156.0 156.0 155.5
j Mw −65.1 −40 −64.8 −65.1 −65.1 −45 −61.8
k Mw −20 −20.1 −20 −20 −40 −15.3
m Mvar 18.3 18.4 18.9 18.7 16.4
* Wrong measurement ID,
†
Input value of the wrong measurement.
Table 9. Normalized residuals and sensitivity for the detection and replacement of bad data.
Case W.ID Normalized
Residual
†
Sensitivity
Value
††
Case W.ID Normalized
Residual
†
Sensitivity
Value
††
4 d 2.98 −0.239 6 b 1.17 - *
f 3.44 0.209 c 2.85 0.057
g 4.58 −0.275 g 2.806 −0.177
j 3.72 −0.233 7 h 3.42 −0.235
5 c 2.88 0.058 j 5.02 −0.304
i 1.0 - * k 2.01 - *
* Normalized residual below a threshold of 2.5,
†
Equation (4),
††
Equation (7).
Figure 9. Comparison of MW and MVAR between true and Case 7.

Energies 2021,14, 2301 14 of 24
4.1.3. Scenario C
This scenario uses test procedures to validate the proposed tap estimation method
in this paper. The simulation condition of this case is identical to scenario B, except for
the location of the wrong data. Table 10 shows the estimated values of the tap positions
and the voltage. I
†
represents the wrong input data of the tap position estimation. Table 11
shows the normalized residuals and sensitivity of the bad data detection and replacement
for the four cases. From the results, the true values of the power system can be accurately
calculated from the wrong data using the proposed process.
Table 10. Results of the bad data processing for Scenario C.
TR
Num. W.ID * Meas.
Type
True
Value
Case 8 Case 9 Case 10 Case 11
I†Est. I †Est. I †Est. I †Est.
1o Tap 11 5 11 9 11 11 16 11
p Volt 153.7 153.7 153.7 153.7 150 153.6
2q Tap 9 9 7 9 5 9 5 9
r Volt 150.4 150.4 150.4 150 150.3 150 150.3
* Wrong measurement ID, †Input value of wrong measurement.
Table 11. Sensitivity and tap position per iteration for Case 11.
Iter. Tap Sensitivity Value Adjust. Tap Iter. Tap Sensitivity Value Adjust. Tap
1 16 Not conv. 1 5 Not conv.
2 16 0.2442 −6 2 5 −2.4429 +3
3 10 −0.0255 +1 3 8 −0.6034 +1
4 11 Full Conv. 4 9 Full Conv.
4.1.4. Scenario D
This scenario compares the performance of the proposed algorithm with the simu-
lation results of state estimation function in PowerFactory (PoF), which consists of four
components such as pre-processing, plausibility check, observability analysis, and non-
linear optimization including the bad data detection [
25
]. Figure A1 shows the modified
IEEE 18 bus test system based on PoF, and the simulation condition of this case is iden-
tical to Case 3 of scenario A. Because of the functional difference between the proposed
algorithm and the PoF, the gap between two programs may occur. In order to configure
the conditions equally between two programs, the location of measurement and standard
deviation are set to the same. Figure 10 shows the voltage magnitude of the true value,
the proposed algorithm and PoF. From the results, the proposed algorithm exhibited good
agreement with the true value. The function and parameters of PoF should influence to
some differences between the proposed algorithm and PoF.
Energies 2021, 14, x FOR PEER REVIEW 15 of 24
Figure 10. Voltage magnitude among the true value, the proposed algorithm and PowerFactory.
4.2. Dynamic Tests
An important factor for dynamic tests is to validate the performance of the proposed
algorithm using the standard of state estimation. For large-scale power systems with a
massive number of network components, a solution may not converge, and some difficul-
ties may arise, such as differences in the performance. In order to maintain the perfor-
mance of state estimation, power system transmission operators, such as ERCOT, PJM,
and NGESO, create state estimation standards w.r.t convergence and the differences be-
tween the measurements and estimations for branch flows and power stations. The dy-
namic test in this study was focused on finding differences between the REMS and the
measurements in KEPS. Table 12 shows various scenarios for validating the state estima-
tion performance in KEPS. The test system was based on the KEPS in 2016. The system’s
total generation was 58,748 MW and 10,878 MVAR, and its load was 57,470 MW and
10,697 MVAR, respectively. The two HVDC systems in the KEPS transmit relatively cheap
electric power from the mainland to the Jeju. As shown in Table 13, the penetration of
renewable energy used in this paper is approximately 6.6%, which consists of generator
with renewable energy of 251.
Table 12. Scenario of a dynamic test on KEPS.
Scenario ID Function Description
E Estimation
- Estimate the voltage and flow
- Evaluate the performance based on ERCOT standard
F Metering accuracy
- Analyze the system at different meter accuracy range
- 100 runs at both MW measurements of line
G Severe event
- Perform the pseudo measurement processing and bad data
processing for severe events
- Suspect of all measurements in station or division
Table 13. Renewable energy model for a dynamic test.
Type Installed Capacity Num. of Models Generation Capacity Facto
r
Connection Voltage
Wind 2161 MW 62 1037 MW 48% 154 kV
Solar 7122 MW 189 2849 MW 40% 22.9 kV
4.2.1. Scenario E
In this scenario, the state estimation was checked to ensure that it meets the originally
specified functions, which calculate the bus voltage, branch flow, and generation based
on the standard deviation of the measurements. In KEPS, the weighting factor of the gen-
erators is more than that of the other devices because the security-constrained economic
Figure 10. Voltage magnitude among the true value, the proposed algorithm and PowerFactory.

Energies 2021,14, 2301 15 of 24
4.2. Dynamic Tests
An important factor for dynamic tests is to validate the performance of the proposed
algorithm using the standard of state estimation. For large-scale power systems with a mas-
sive number of network components, a solution may not converge, and some difficulties
may arise, such as differences in the performance. In order to maintain the performance of
state estimation, power system transmission operators, such as ERCOT, PJM, and NGESO,
create state estimation standards w.r.t convergence and the differences between the mea-
surements and estimations for branch flows and power stations. The dynamic test in this
study was focused on finding differences between the REMS and the measurements in
KEPS. Table 12 shows various scenarios for validating the state estimation performance in
KEPS. The test system was based on the KEPS in 2016. The system’s total generation was
58,748 MW and 10,878 MVAR, and its load was 57,470 MW and 10,697 MVAR, respectively.
The two HVDC systems in the KEPS transmit relatively cheap electric power from the
mainland to the Jeju. As shown in Table 13, the penetration of renewable energy used
in this paper is approximately 6.6%, which consists of generator with renewable energy
of 251.
Table 12. Scenario of a dynamic test on KEPS.
Scenario ID Function Description
EEstimation - Estimate the voltage and flow
- Evaluate the performance based on ERCOT standard
FMetering accuracy
- Analyze the system at different meter accuracy range
- 100 runs at both MW measurements of line
G Severe event
- Perform the pseudo measurement processing and
bad dataprocessing for severe events
- Suspect of all measurements in station or division
Table 13. Renewable energy model for a dynamic test.
Type Installed
Capacity
Num. of
Models Generation Capacity
Factor
Connection
Voltage
Wind 2161 MW 62 1037 MW 48% 154 kV
Solar 7122 MW 189 2849 MW 40% 22.9 kV
4.2.1. Scenario E
In this scenario, the state estimation was checked to ensure that it meets the originally
specified functions, which calculate the bus voltage, branch flow, and generation based on
the standard deviation of the measurements. In KEPS, the weighting factor of the generators
is more than that of the other devices because the security-constrained economic dispatch
uses the state estimation results. The state estimation adopting the proposed algorithm in
this study converged after nine iterations. The voltage convergence tolerance was
0.005 p.u.
Table 14 shows a summary of the state estimation, including the number of bad data
processing instances, the value of the largest bus mismatch, and the suspect ratio. Because
of the suspect measurements of the HVDC system, the number of iterations was creased. In
the case of >20 MW and 20 MVAR, the number of bus mismatches of the MW and MVAR
was 0 and 5, respectively. Table 15 shows the MW and MVAR summary of the generation
and load for the state estimation results and measurements. The MW generation between
the estimation results and measurements had a small difference because of the different
weighting factors, which represent the reciprocal of the standard deviation.
Figure 11
shows the voltage magnitude of the 345 kV between the telemetered voltage and the state
estimation. Figures 12 and 13 show the generator unit for the 50 largest equipment and
active power of the branch flow, respectively. The results of this scenario exhibited good
agreement with the REMS and the measurements.

Energies 2021,14, 2301 16 of 24
Table 14. Summary of the state estimation.
Item Results
Number of iteration/Number of state variable 9/8788
Number of bad data processing 12
Largest bus mismatch of MW/Mvar 7 MW/36 Mvar
Suspect rate 3%
Table 15. Comparison between the measurements and the state estimation.
Item Measurement State Estimation Difference
Generation MW 58,748 58,749 1
Generation Mvar 10,878 10,532 345
Load MW 57,470 57,870 400
Load Mvar 10,697 9728 968
Energies 2021, 14, x FOR PEER REVIEW 16 of 24
dispatch uses the state estimation results. The state estimation adopting the proposed al-
gorithm in this study converged after nine iterations. The voltage convergence tolerance
was 0.005 p.u. Table 14 shows a summary of the state estimation, including the number of
bad data processing instances, the value of the largest bus mismatch, and the suspect ratio.
Because of the suspect measurements of the HVDC system, the number of iterations was
creased. In the case of >20 MW and 20 MVAR, the number of bus mismatches of the MW
and MVAR was 0 and 5, respectively. Table 15 shows the MW and MVAR summary of
the generation and load for the state estimation results and measurements. The MW gen-
eration between the estimation results and measurements had a small difference because
of the different weighting factors, which represent the reciprocal of the standard devia-
tion. Figure 11 shows the voltage magnitude of the 345 kV between the telemetered volt-
age and the state estimation. Figures 12 and 13 show the generator unit for the 50 largest
equipment and active power of the branch flow, respectively. The results of this scenario
exhibited good agreement with the REMS and the measurements.
Table 14. Summary of the state estimation.
Item Results
Number of iteration / Number of state variable 9 / 8,788
Number of bad data processing 12
Largest bus mismatch of MW/Mvar 7 MW/36 Mvar
Suspect rate 3%
Table 15. Comparison between the measurements and the state estimation.
Item Measurement State Estimation Difference
Generation MW 58,748 58,749 1
Generation Mvar 10,878 10,532 345
Load MW 57,470 57,870 400
Load Mvar 10,697 9728 968
Figure 11. Voltage magnitude of a 345-kV bus for the estimation results and measurements.
Figure 11. Voltage magnitude of a 345-kV bus for the estimation results and measurements.
Energies 2021, 14, x FOR PEER REVIEW 17 of 24
Figure 12. Generation for the estimation results and measurements of the 50 largest units.
Figure 13. Branch flow for the estimation results and measurements of the 50 largest trans. lines.
The performance of the state estimation of the REMS was evaluated using the refer-
ence of the state estimation of ERCOT [26], which is among the system operators in the
United States with a high penetration of renewable energy. The installed capacity of re-
newable energy in 2020 year is 35,114 MW. The reasons for selecting the reference of the
state estimation of ERCOT in this paper are as follow.
First, Korea Power eXchange (KPX) operates the power system based on EMS. To
analyze and maintain the performance of generation, voltage and power flow, KPX has
established the reference of state estimation based on ERCOT and other ISOs. KPX up-
dates the EMS function by benchmarking ERCOT cases in terms of the penetration of re-
newable energy. Second, Korea has recently announced the target of 78.1 GW of renewa-
ble energy by 2034. Since most of the large-scale renewable energy is wind power, ERCOT
with high penetration of wind power is a good reference. Third, loads are concentrated in
the metropolitan area in Korea. The transmission lines connecting the metropolitan area
Figure 12. Generation for the estimation results and measurements of the 50 largest units.

Energies 2021,14, 2301 17 of 24
Energies 2021, 14, x FOR PEER REVIEW 17 of 24
Figure 12. Generation for the estimation results and measurements of the 50 largest units.
Figure 13. Branch flow for the estimation results and measurements of the 50 largest trans. lines.
The performance of the state estimation of the REMS was evaluated using the refer-
ence of the state estimation of ERCOT [26], which is among the system operators in the
United States with a high penetration of renewable energy. The installed capacity of re-
newable energy in 2020 year is 35,114 MW. The reasons for selecting the reference of the
state estimation of ERCOT in this paper are as follow.
First, Korea Power eXchange (KPX) operates the power system based on EMS. To
analyze and maintain the performance of generation, voltage and power flow, KPX has
established the reference of state estimation based on ERCOT and other ISOs. KPX up-
dates the EMS function by benchmarking ERCOT cases in terms of the penetration of re-
newable energy. Second, Korea has recently announced the target of 78.1 GW of renewa-
ble energy by 2034. Since most of the large-scale renewable energy is wind power, ERCOT
with high penetration of wind power is a good reference. Third, loads are concentrated in
the metropolitan area in Korea. The transmission lines connecting the metropolitan area
Figure 13. Branch flow for the estimation results and measurements of the 50 largest trans. lines.
The performance of the state estimation of the REMS was evaluated using the reference
of the state estimation of ERCOT [
26
], which is among the system operators in the United
States with a high penetration of renewable energy. The installed capacity of renewable
energy in 2020 year is 35,114 MW. The reasons for selecting the reference of the state
estimation of ERCOT in this paper are as follow.
First, Korea Power eXchange (KPX) operates the power system based on EMS. To
analyze and maintain the performance of generation, voltage and power flow, KPX has
established the reference of state estimation based on ERCOT and other ISOs. KPX updates
the EMS function by benchmarking ERCOT cases in terms of the penetration of renewable
energy. Second, Korea has recently announced the target of 78.1 GW of renewable energy
by 2034. Since most of the large-scale renewable energy is wind power, ERCOT with
high penetration of wind power is a good reference. Third, loads are concentrated in
the metropolitan area in Korea. The transmission lines connecting the metropolitan area
and the non-metropolitan area are very important. In particular, a thyristor controlled
series capacitor (TCSC) is installed and a special protection system (SPS) is operated in
preparation for an accident on a 765 kV line.
Table 16 shows the performance requirements of the state estimation of ERCOT for
the convergence, branch flow, and voltage. Table 17 shows the performance requirements
of the state estimation of CASIO [27].
Table 16. State estimation performance requirements of ERCOT [26].
Item Description
Convergence 98% of runs during a 1-month period.
Branch flow
On all transmission elements >100 kV, the difference between estimation
and measurement shall be <10 MW or 10% of the associated emergency
rating on at least 95% of samples measured in a 1-month period.
Critical flow
The difference between estimation and measurement shall be <3% of the
associated emergency rating on at least 95% of samples measured in a
1-month period.
Voltage
For the 20 most important station voltage points, the telemetered voltage
minus estimation shall be within 2% of the telemetered measurement on
at least 95% of samples measured in a 1-month period.

Energies 2021,14, 2301 18 of 24
Table 17. State estimation performance requirements of CASIO [27].
Item Description
Solution Chi-square percentile >90% and normalized residuals of measurement <3.
Negative load
and
generation
The number of the negative load shall be <50. The summation of the
negative load shall be <100 MW or the ratio between the negative load and
the system load shall be <2%. The number of the negative generation units
shall be <50, and the summation of the negative generation shall be <50 MW.
Table 18 shows the performance of the state estimation adopting the proposed algo-
rithm based on the reference of ERCOT. The maximum differences for the branch flow,
critical branch flow, and voltage were 2.17%, 0.4%, and 0.92%, respectively. As shown in
Table 18, although the comparative study was performed with one dataset, the proposed
algorithm agrees well with the reference of ERCOT.
Table 18. Results for testing the performance requirements based on ERCOT.
Item Description Ref. Results
Branch flow All trans. elements ≥154 kV 10% of emer. rating Max: 2.17%
SE: 762/ME: 710[MW]
Critical flow All 765 kV line & interface line
between metropolitan 3% of emer. rating Max: 0.4%
SE: 1674/ME: 1637[MW]
Voltage All voltages ≥345 kV 2% of tele. voltage Max: 0.92%
SE: 754/ME: 761[kV]
4.2.2. Scenario F
In order to analyze the telemetry accuracy effect on the proposed algorithm, the state
estimation was simulated for different meter suspects ranging from 3% to 10%. The MW
measurements on both sides of the transmission line were randomly selected as data
with suspect flags, and the state estimation was run for 100 times. Because the number
of measurements associated with the transmission line is largest in KEPS, this scenario
selected its MW measurement. The simulation condition of this scenario is identical
to scenario E, except for the additional suspected data. The total number of metering
points of the transmission line was 4378, and the available data with good flags was 4320.
This scenario was assigned as the suspect data from 3% to 10% of 4320. Table 19 shows
the average values of the estimation results and measurements for the branch flow and
generation, respectively.
Table 19. Performance of the state estimation for the metering accuracy.
Item Measurement Error
3% 5% 7% 10%
Average of all transmission lines difference between
estimation and telemetered data for MW 2.97 2.98 2.99 3.0
Number of divergence 3 4 6 12
Average of largest bus mismatch for MW 11.42 11.26 11.07 10.80
Average of total generation difference between estimation
and telemetered data for MW 5.55 5.86 6.14 7.42
With the increase in the range of suspects, the average difference between the estima-
tion data and the measurements increased, as shown in Figures 14 and 15. Overall, this
scenario shows that the proposed algorithm can be accurately utilized for datasets with
high suspect ratios.

Energies 2021,14, 2301 19 of 24
Energies 2021, 14, x FOR PEER REVIEW 19 of 24
except for the additional suspected data. The total number of metering points of the trans-
mission line was 4378, and the available data with good flags was 4320. This scenario was
assigned as the suspect data from 3% to 10% of 4320. Table 19 shows the average values
of the estimation results and measurements for the branch flow and generation, respec-
tively.
Table 19. Performance of the state estimation for the metering accuracy.
Item Measurement Error
3% 5% 7% 10%
Average of all transmission lines difference between estima-
tion and telemetered data for MW 2.97 2.98 2.99 3.0
Number of divergence 3 4 6 12
Average of largest bus mismatch for MW 11.42 11.26 11.07 10.80
Average of total generation difference between estimation
and telemetered data for MW 5.55 5.86 6.14 7.42
With the increase in the range of suspects, the average difference between the esti-
mation data and the measurements increased, as shown in Figures 14 and 15. Overall, this
scenario shows that the proposed algorithm can be accurately utilized for datasets with
high suspect ratios.
Figure 14. Branch flow between the estimation results and measurements for the SE 100 runs.
Figure 14. Branch flow between the estimation results and measurements for the SE 100 runs.
Energies 2021, 14, x FOR PEER REVIEW 20 of 24
Figure 15. Generation between the estimation results and measurements for the SE 100 runs.
4.2.3. Scenario G
In this simulation, all the measurements in the same station and a metropolitan area
division were assumed to have suspect flags. This test was required to guarantee the per-
formance of the bad data processing and pseudo processing method in the case of a severe
event involving the loss of a large amount of data. On the basis of the same conditions as
scenario E, all the measurements in the division of Nam-Seoul were set to suspects. The
division of Nam-Seoul has 63 stations, 4 of which are 345-kV stations. For the 63 substa-
tions in this division, the suspect values of the voltage, branch, and injection were replaced
by pseudo measurements, such as the values calculated using an economic dispatch and
the BLDF. Nam-Seoul division in the metropolitan area is the largest load division. If the
contingency in this division occurred, the impact is relatively more sensitive than other
divisions. Table 20 shows the estimated values using the telemetered data and suspect
data. Although the number of suspect stations increases, the average difference between
the estimation results and measurements for voltage, MW, MVAR remains similar. In the
case with 63 of suspect station, the average difference is 18.20 MW and 8.6 MVAR, and
the largest difference is 265.72 MW and 65.71 MVAR, respectively. Because there are a lot
of load, transformer and transmission line in 154-KV and 345-kV stations, the largest dif-
ference has some big values despite of the small average difference. In order to solve this
problem, the difference could be decreased by correcting the parameters such as pseudo
measurement and standard deviation. System operator should be adjusted by controlling
the standard deviation which causes the increasing of the average difference and the de-
creasing of the largest difference. Figure 16 shows the active power flow of the transmis-
sion line in the Nam-Seoul division between the estimation results and measurements.
The results of the state estimation showed that the proposed algorithm can exactly esti-
mate the actual values from the suspected data and that it can be correctly operated during
these severe situations.
Table 20. Comparative results for severe events.
Item Description Volt. Level Unit
Numbe
r
of Suspect Station
4 31 63
Figure 15. Generation between the estimation results and measurements for the SE 100 runs.
4.2.3. Scenario G
In this simulation, all the measurements in the same station and a metropolitan area
division were assumed to have suspect flags. This test was required to guarantee the
performance of the bad data processing and pseudo processing method in the case of
a severe event involving the loss of a large amount of data. On the basis of the same
conditions as scenario E, all the measurements in the division of Nam-Seoul were set to

Energies 2021,14, 2301 20 of 24
suspects. The division of Nam-Seoul has 63 stations, 4 of which are 345-kV stations. For
the 63 substations in this division, the suspect values of the voltage, branch, and injection
were replaced by pseudo measurements, such as the values calculated using an economic
dispatch and the BLDF. Nam-Seoul division in the metropolitan area is the largest load
division. If the contingency in this division occurred, the impact is relatively more sensitive
than other divisions. Table 20 shows the estimated values using the telemetered data and
suspect data. Although the number of suspect stations increases, the average difference
between the estimation results and measurements for voltage, MW, MVAR remains similar.
In the case with 63 of suspect station, the average difference is 18.20 MW and 8.6 MVAR,
and the largest difference is 265.72 MW and 65.71 MVAR, respectively. Because there
are a lot of load, transformer and transmission line in 154-KV and 345-kV stations, the
largest difference has some big values despite of the small average difference. In order to
solve this problem, the difference could be decreased by correcting the parameters such
as pseudo measurement and standard deviation. System operator should be adjusted by
controlling the standard deviation which causes the increasing of the average difference
and the decreasing of the largest difference. Figure 16 shows the active power flow
of the transmission line in the Nam-Seoul division between the estimation results and
measurements. The results of the state estimation showed that the proposed algorithm
can exactly estimate the actual values from the suspected data and that it can be correctly
operated during these severe situations.
Table 20. Comparative results for severe events.
Item Description Volt. Level Unit
Number of Suspect Station
4 31 63
Voltage
Average of voltage difference
between estim. and tele.
345 kV 0.73 0.62 0.59
154 kV 0.97 1.03 1.08
Largest voltage difference All kV 2.10 2.30 2.59
Flow
Average of flow difference
between estim. and tele. All Mw 2.36 4.20 18.20
Mvar 3.64 5.02 8.60
Largest flow difference All Mw 21.51 30.99 265.72
Mvar 62.42 45.36 65.71
Ratio Ratio of good data - % 96 93 90
Energies 2021, 14, x FOR PEER REVIEW 21 of 24
Voltage
Average of voltage difference between estim.
and tele.
345 kV 0.73 0.62 0.59
154 kV 0.97 1.03 1.08
Largest voltage difference All kV 2.10 2.30 2.59
Flow
Average of flow difference
b
etween estim. and
tele. All Mw 2.36 4.20 18.20
Mvar 3.64 5.02 8.60
Largest flow difference All Mw 21.51 30.99 265.72
Mvar 62.42 45.36 65.71
Ratio Ratio of good data - % 96 93 90
Figure 16. Estimation results and measurements for the branch flow in the Nam-Seoul division.
5. Conclusions
In this paper, a methodology for implementing the state estimation and enhancing
the accuracy in large-scale power systems including various renewable energy resources
is presented, and it showed accurate and reliable performance in the studied REMS.
First, the application common database for analyzing the power system is proposed
based on node-breaker model, bus-branch model and linked list method. Renewable en-
ergy was modeled by the basis of the point of data acquisition, the type of renewable en-
ergy, and the voltage level of the bus-connected renewable energy. The connectivity
model of a three winding transformer using a switching device is proposed to overcome
the lack of measurements related to transformer.
Second, the procedure of analyzing the topology error associated with the three-
winding transformer is proposed based on simple heuristic method which could be ana-
lyzed and identified the suspect measurements using the condition of feasibility check.
This is a pre-processing, which assign the active power of line to the renewable energy
generator, calculate the bus mismatch, and identify the suspect measurements and buses.
Third, the state estimation based on the fast-decoupled WLS approach and bad data pro-
cessing is implemented. The bad data processing based on two stages is proposed. One
stage is an inner-processing, which estimates the MW, MVAR, voltage, and tap position
through a normalized residue and modified sensitivity calculation. Two stage is an outer-
processing, which analyze the validation of the bad measurement selected in inner pro-
cessing using the condition of Kirchhoff’s current law. Through the two steps, the pro-
posed algorithm could be estimated for the power system with a lack of the measurements
associated with a transformer because of the expanding renewable energy.
Figure 16. Estimation results and measurements for the branch flow in the Nam-Seoul division.

Energies 2021,14, 2301 21 of 24
5. Conclusions
In this paper, a methodology for implementing the state estimation and enhancing
the accuracy in large-scale power systems including various renewable energy resources is
presented, and it showed accurate and reliable performance in the studied REMS.
First, the application common database for analyzing the power system is proposed
based on node-breaker model, bus-branch model and linked list method. Renewable
energy was modeled by the basis of the point of data acquisition, the type of renewable
energy, and the voltage level of the bus-connected renewable energy. The connectivity
model of a three winding transformer using a switching device is proposed to overcome
the lack of measurements related to transformer.
Second, the procedure of analyzing the topology error associated with the three-
winding transformer is proposed based on simple heuristic method which could be ana-
lyzed and identified the suspect measurements using the condition of feasibility check. This
is a pre-processing, which assign the active power of line to the renewable energy generator,
calculate the bus mismatch, and identify the suspect measurements and buses. Third, the
state estimation based on the fast-decoupled WLS approach and bad data processing is
implemented. The bad data processing based on two stages is proposed. One stage is an
inner-processing, which estimates the MW, MVAR, voltage, and tap position through a
normalized residue and modified sensitivity calculation. Two stage is an outer-processing,
which analyze the validation of the bad measurement selected in inner processing using
the condition of Kirchhoff’s current law. Through the two steps, the proposed algorithm
could be estimated for the power system with a lack of the measurements associated with
a transformer because of the expanding renewable energy.
Finally, through static and dynamic tests, a comprehensive set of simulation results
have shown that the proposed algorithm can provide accurate power system estimations.
A static test was also performed to validate the individual function of the state estimation
based on a modified IEEE 18-bus test system. Also, a comparative study among the
proposed algorithm, PowerFactory, and measurement is performed. Furthermore, the
dynamic test results exhibited good agreement between the solution of the state estimation
and the telemetered data with some variances and severe events. The validation test for
assessing the performance requirements based on ERCOT is carried out. Although the
measurement consists of a set of data received from SCADA and RTU, the state estimation
was simulated for different meter suspects ranging from 3% to 10%. For the suspect data
from 3% to 10% of 4320, the rate of convergence has decreased from 97% to 88% with small
difference between the estimated value and measurement.
The limitations of the methodology implemented in this paper and various simulation
is detailed below:
•
The proposed algorithm based on the WLS technique, inner and outer bad data pro-
cessing, pseudo measurement processing, and topology error processing, etc. has
advantage of fast execution speed, convergence and computationally easy. However,
the WLS technique is sensitive to the initial condition and the data quality of mea-
surement. In order to solve these problems, pseudo measurement and topology error
processing should be applied.
•
The dynamic test is analyzed using a set of measurements received from SCADA and
RTU. Especially, a variety of analyzes of cases involving measured renewable energy
with various penetration level was lacking. The installed capacity of renewable energy
is 19,700 MW. Most of the renewable energy is the distributed generation connected to
distribution system.
•
As renewable energy expanded, the effect of the components associated with renew-
able energy will be increasing in the platform of the EMS. Every time state estimation
is performed, the results of state estimation could be changed because of the large vari-
ability of renewable energy. To deal with this situation, a robust algorithm is needed.
To enhance the accuracy and performance of the proposed algorithm, the future
research of this paper is described as follows:

Energies 2021,14, 2301 22 of 24
•
The WLS method based on a full coupled gain matrix, QR decomposition, and PMU
will be studied. Full gain matrix and QR decomposition should increase the numerical
stability and convergence characteristics.
•
The extensive case study for increasing penetration of renewable energy will be
analyzing after various sets of measurements received from electric utility.
•
To create the input data with high penetration and various power system condi-
tions, the replica data creation based on a real time digital simulator (RTDS) will
be considered.
Author Contributions:
Conceptualization, methodology, investigation, and software, Y.-S.C.; valida-
tion, formal analysis, and data curation, Y.-H.C.; writing, Y.-S.C. and Y.-H.C. All authors have read
and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments:
This work was supported by “Human Resources Program in Energy Technology”
of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial
resource from the Ministry of Trade, Industry & Energy, Republic of Korea. (No. 20194010201760).
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1 shows the requirement for modeling the renewable energy. Renewable
energy consists of generator, transformer and transmission line.
Table A1. Power flow model of the renewable energy.
Equipment List Contents
Generator
Capacity(Mbase) Farm Capacity
Active/Reactive power Measured data
P min/max Max = Installed Cap, Min = 0
Q min/max ±Pmax ∗tancos−1PF
Control mode Voltage control
Power factor PV 1.0, Wind 0.95
Target voltage Measured data
Zsource
Fault contribution
- Wind 0.67: Rated current*1.5 times)
- PV 1.0: Rated current*1 times)
Transformer Impedance/Tap Impedance (~6%), Tap Ratio
Line Impedance Impedance (If data none, the impedance of
20 km line apply)
Figure A1 shows the modified IEEE 18 bus test system based on PowerFactory which
should be compared the validation of the proposed algorithm.

Energies 2021,14, 2301 23 of 24
Energies 2021, 14, x FOR PEER REVIEW 23 of 24
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1 shows the requirement for modeling the renewable energy. Renewable en-
ergy consists of generator, transformer and transmission line.
Table A1. Power flow model of the renewable energy.
Equipment List Contents
Generator
Capacity(Mbase) Farm Capacity
Active/Reactive power Measured data
P min/max Max = Installed Cap, Min = 0
Q min/max ±𝑃 ∗tan(cos 𝑃𝐹)
Control mode Voltage control
Power factor PV 1.0, Wind 0.95
Target voltage Measured data
Zsource
Fault contribution
- Wind 0.67: Rated current*1.5 times)
- PV 1.0: Rated current*1 times)
Transformer Impedance/Tap Impedance (~6%), Tap Ratio
Line Impedance
Impedance (If data none, the impedance of 20 km line ap-
ply)
Figure A1 shows the modified IEEE 18 bus test system based on PowerFactory which
should be compared the validation of the proposed algorithm.
Figure A1. Modified IEEE 18-bus test system based on PowerFactory.
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Figure A1. Modified IEEE 18-bus test system based on PowerFactory.
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