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# Fuzzy based multi-line Power Outage Control System

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Transmission lines are among the most important power grid equipment in which their removal can lead to subsequent outages. One of the situations which lead to blackouts is multiple line outages. Therefore, multiple line outage detections (MLOD) are a necessity in the power system for protection actions. This paper proposes a fuzzy inference system (FIS) to identify multiple line outages. The proposed FIS can determine multiple line outage conditions by continuously monitoring the lines circuit breakers (CBs) status. For this purpose, by introducing the line CBs status as inputs, the status of the predefined line outage scenario is determined as the output of the proposed FIS. This study is helpful for power system researchers to make decisions about power system protection. The proposed fuzzy MLOD system has the advantage of high precision in applying the fuzzy system as an artificial intelligence tool. For the reason of problem dimensions limitations, in this paper, just single and multi-line outage contingencies are considered for the MLOD problem although the proposed method can be extended to detect triple and more line outage contingencies by definition the additional fuzzy rules. The proposed fuzzy MLOD system is tested on the IEEE 5-bus system and the results are presented.
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Fuzzy based multi-line Power Outage Control
System
1Duhok Polytechnic University, Duhok Iraq
2Hohai University Changzhou, Jiangsu China
3Petroweld Kurdistan Erbil, Iraq
4Pakistan Steel Mills Karachi Pakistan
ABSTRACT:Transmission lines are among the most important power grid equipment in which their removal can
lead to subsequent outages. One of the situations which lead to blackouts is multiple line outages. Therefore,
multiple line outage detections (MLOD) are a necessity in the power system for protection actions. This paper
proposes a fuzzy inference system (FIS) to identify multiple line outages. The proposed FIS can determine multiple
line outage conditions by continuously monitoring the lines circuit breakers (CBs) status. For this purpose, by
introducing the line CBs status as inputs, the status of the predefined line outage scenario is determined as the output
of the proposed FIS. This study is helpful for the power system researchers to make decisions about power system
protection. The proposed fuzzy MLOD system has the advantage of high precision in applying the fuzzy system as
an artificial intelligence tool. For the reason of problem dimensions limitations, in this paper, just single and multi-
line outage contingencies are considered for the MLOD problem although the proposed method can be extended to
detect triple and more line outage contingencies by definition the additional fuzzy rules. The proposed fuzzy MLOD
system is tested on IEEE 5-bus system and the results are presented.
KEYWORDS:Multiple line outage, detection, fuzzy system
1. INTRODUCTION
Getting the smooth and uninterrupted power supply from power houses to the home or the vicinity is much crucial.
It is also important to have sufficient transmission line protection arrangements. Nowadays, extraordinary computer
tools are available to model the power system network in every aspect.The power systems have numerous protection
challenges which are been seriously considered, as power transmission lines are inevitably exposed to a variety of
extreme weather disturbances, equipment aging, human error and even malicious attacks. In fact, due to unexpected
contingencies and the evolving nature of power grids, the completely reliabl e and safe operation cannot be
achieved.because power system engineers try to design a reliable system.
The development of the protection system for power transmission lines can be divided into three stages. Specifically,
conventional protection is mainly based on an electro-mechanical protection relay for the separation of overloading
branches[1]. Despite the high reliability and simplicity of the construction, these relays need to be calibrated
periodically in the absence of directional features. With the advent of computer features, the second protection stage
as the advanced intelligent control algorithms can be applied to protect power grids[2]. In addition, the use of the
Global Positioning System (GPS) is a remarkable milestone that enables engineers to synchronize time precisely and
obtain global phase information for the protection of the wider area. The availability of global information on power
systems makes it possible to establish a systematic approach to dealing with catastrophic scenarios in large-scale
power grids. The Special Protection Scheme (SPS) is therefore proposed to reduce global stress by separating power
systems from several islands and isolating faulty areas according to predetermined actions.
Hundreds of transmission lines can be connected to the corresponding generating stations by a single line, including
every system supporting component. There are a variety of real-time technologies in power and energy systems. The
issue of line outage in power system whether planned such as network maintenance operations or unplanned such as
fault situations may cause some problem for the protection system. In other words, if these outage conditions are not
included in the protection plan, it is likely to cause outage of other system elements i.e. loads or resources. If the
protection system operates properly, after removal of multiple lines, the rest of the system will remain stable,
otherwise some miscoordination may occur between system protective elements and consequently the system will
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encounter regional or wide blackouts. Therefore,the detection of these situations has significant impact on
preventing the protection miscoordinations and blackouts[3].
This study is being conducted to investigate the maleficent impairments in Multi-line Power Outage Control system
using a fuzzy based sagacious approach through IEEE 57-bus system with the help of IEEE 5- bus sten.We adopted
the Line Breaker (LB) [4], switches and the status of all LBs for all lines is expressed in fuzzy set notations. We
conducted MATLAB simulation to ratify the performance of the proposed system similar to the realtime scenarios.
Further, a balanced phase power system is also modeled in theSimulink performance.Figure 1, illustrates the overall
power outrage mechanism accordingly.Initially, a flooded decision identifies the status of the transmission lines. The
analog signals are obtained from the power lines. It is considered that a power line meter is associated with each pair
of LBs in the single line. For clarification, a line with two LBs is shown in figure 2, along with the corresponding
power flow meter.
Figure 1. Information flow chart of fuzzy basedmulti-line Power Outage Control System
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Figure 2. Line breaker
Initially, the analog signals will be initially transmitted to the central system via the TCP / IP network, allowing all
protection, control, measuring and monitoring functions within the power supply system to be integrated. The analog
signals are then converted into digital values and used to determine the current line status for the system. The
converter is used for structural analysis to generate and transmit digital data. The further discussion is given in the
proposed section.
2. RELATED WORK
The numerous researches are available in literates which issued multiple line outage detection (MLOD) methods [5],
in the power systems. Most of the researches have focused on power flow measurements analysis for MLOD
problem.The author [6], proposed a double-line outage detection method in the power systemmainly based on AC
power flow equations and injected power. This system has some limitation during operation. The changes are
declared using the AC model by power injections and consequently the outage can be detected. Another author in
[7],introduced a double-line outage detection scheme based on load flow. For this purpose, initially a reactance
model of system is derived. Afterward, the outage contingencies are detected t hrough analysis and the reactance
matrix values are obtained from performing load flow. The status of the connection lines can be declared through the
reactance matrix.
In [3] and [4] the probability and estimation techniques are used for MLOD problem. In [3], first the system is
modeled and load flow is performed. Then estimation of distribution algorithm is used to estimate the outage of lines
based on threshold between outage and normal status of lines. In [4] first the probability of occurrence for each
multiple line outage configuration is calculated based on overloading index and skip the outage configurations with
too low probability. Then, the lines which are in outage conditions are detected by monitoring the modifications in
reactance matrix for remained set of outage configurations. The MLOD problem [8], has been discussed depends on
phase angles analysis. Data from phasor measurement units (PMUs) is analyzed, and the status of lines is calculated
as a result of the analysis.
Despite of substantialoutcome, most of the existing approaches for line outage detection suffers from two drawbacks
which are high computational complexity and the difficulty of analyzing the results for the users. The high
computational complexity especially in wide grids limits the MLOD only for the case of single line or double-line
outages. On the other hand, the speed of analyzing data and making subsequent decisions in central system are much
important. Considering these drawbacks, the present paper proposes a fuzzy system which overcome both
mentioned drawbacks. The proposed fuzzy system can be extended for wide grids to detect more line outage
contingencies and can be used as a comprehensive graphical user inference (GUI) in central system for more simple
and fast decision making by users.
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2.1 Architecture of IEEE 57 bus system
It is an American based power system and the bust network consists of 63 transmission lines, 7 generators, 42 loads
and 17 tap-setting transformers[9]. According to the available data, the transmission line does not have any active
and reactive power limits.The power system consists of two segments, the first is the Transmission segment and the
second is the Distribution segment. The first segment consists of generators, 1 to 17 voltage busses of 138 kv (line-
line) and the Tap Setting Transformers, while second segment incorporates 18 to 57 voltage busses of 69kv (line-
line) and distribution loads as shown in figure 3.This model has three major liabilities; First, review the structure of
a power system and the original data to estimate the number of components required and the rating.
Figure 3. TheIEEE 57 bus architecture system
3. MULTI LINE OUTAGE ANALYSIS
A protection system is neededmaking the power system monitored and set plans to help it encounter the likely
scenarios that would happen. Cascade outages [10], are usually uncontrollable and can be very wide. Therefore, it is
necessary to make the protection system for special preparations. One of these situations which might leads to
cascade outages is the multi-line outage. The importance of multi-line outage is considered as the voltage magnitude
at buses under single line and multi-line outage contingencies in IEEE 5 -bus system can be taken therefore, the
multi-line outage has more effect on voltage magnitude than single line outage. It can be observed that there is an
obvious voltage drop in multi-line outage scenarios versus single line outage. Multi line outage can be occurred in
the situation when two lines are disconnected due tofaults or maintenance operations. In such situations, the other
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lines would encounter overloading and then may disconnect since the control system are not planned to action in
overload and low-voltage situations. Eventually, the other lines are disconnected one after another and a cascading
outage would happen.
4.PROPOSED LINE OUTAGE DETECTION
In order to conduct our finding, the multi-line outage detection mechanism mainly based on interface between the
lines CBs status and central system has been considered. The main functional blocks of our proposal isshown in
Figure 4.
Figure 4. The proposed system architecture
The lines statusis identified mainly by the fuzzy logic block and thereforethe analog signals are being obtained from
the power flow meters whileeach pair of CBs in the single line are associated with power flow meter. Initially, the
line with two CBs and the corresponding power flow meter is being monitored then the analog signals are sent to
central system using high speed Ethernet through IEC 61850 TCP/IP protocol [11]. The IEC 61850 is a global
standard for Ethernet-based communication in power system. It enables integration of all protection, control,
measurement and monitoring functions within the power system. Therefore, providing a real times communication
in IEC 61850 protocol, guarantees the speed of the transferring data obtained from the CBs to central system for
further actions.The analog signals in central system converts to digital values by converter to be used as inputs for
the fuzzy system determining the scenario status. Structurally, the converter is used to generate and transfer digital
data for further analysis. Further, the details of our proposed system have been structured with every thick and thin
as
4.1.Line CBs Status
In this paper, the line CBs status are used as an index for line outage detection. Therefore, in order to model the two
possible status for CB, first the active power flow passing through a CB is modeled using a dc approximation as
follows [12]:
Fij = Bεi− θj) (1)
Where 𝐹𝑖𝑗 is the active power flow through the considered CBs; 𝜃𝑖 and 𝜃
𝑗are the voltage angle at bus i and j,
respectively and𝐵𝜀 is the susceptance. Then, a binary variable is used in Equation (2) to model the two status of CB
that becomes [13]:
Fij = UijBε(θi− θj) (2)
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A value of 𝑈𝑖𝑗equal to 0 identifies an open CB, whereas 1 value indicates a closed CB. The CB status consider the
power flow through the line isgiven in figure 5:
(a) (b)
Figure 5. CB status (a) closed (b) open
It can be observed that𝑈𝑖𝑗 = 0 implies null flow i.e., the open CBs conditions. On the other hand,𝑈𝑖𝑗 = 1 implies the
closed CBs conditions. In other word, there is a small voltage angle difference between buses i and j and
consequently apower flow between two buses.
Therefore, according to the definition of one binary variable as 𝑈𝑖𝑗 with two status, the line CBs status can be
detected. Depending on the binary variable𝑈𝑖𝑗 which is determined by the power flow passing through the CBs,
status of the line can be detected. Therefore, for a power system with n lines, the nbinary values are defined for the
lines CBs status such that n indicates a set of CBs in each individual line at each side of the line. A decision making
system is crucially required to receive the binary values from the converter and detect the lines status. Fuzzy
inference system (FIS) is a powerful decision making tool for this purpose. The FIS is simulated in MATLAB fuzzy
toolbox.
4.2.Fuzzy Inference System
The fuzzy interface system (FIS) is being used to detect the line outage contingencies as a decision making tool. A
typical fuzzy logic system consists of a fuzzification component, an inference engine, and a defuzzificaton
component.
The inference engine is a set of IFTHENrules which are made up of linguistic variables and whose consequences
are associated with fuzzy membershipfunctions[14]. In fuzzification block, t he input convertsto linguistic variables
by assigning proper membershipvalues which is required in order to activate the rules. The inference engine matches
all the rules with the inputs, aggregatesthe weighted output of the rules, and generates a possibility distributionof the
values in an output space. The defuzzification block, convertthe distribution into a single crisp value using some
defuzzification methods.
The most important types of FIS are Mamdani fuzzy inference and Takagi Sugeno (TS) fuzzy system[15].
Considering the linear dependence of each rule on the input variable of the system, the Sugeno method is ideal for
multiple linear functions. Also, flexibility in system design and adequate for multiple input multiple output (MIMO)
systems[16], are the main reasons adopting the Sugeno method for proposed system. The typical fuzzy rule i n F IS
has the following format:
IF x is A and y is B THEN z=f(x,y) (3)
Where Aand B are fuzzy sets and z is polynomial or a constant. Such fuzzy rule bases are described by membership
functions in which the input fuzzy sets are form the left hand sides and the output fuzzy sets are form the right hand
sides. Thus, the line status is detected by the measurements obtained from power flow meters continuously. All
theinformation is being converted to binary values and utilize for defining the membership functions in the proposed
fuzzy system. The main steps of our proposed fuzzy system are listed as:
Binary values of CBs status are expressed in fuzzy set notations.
Define fuzzy "IF-THEN" rules for lines CBs status.
If the CBs of any line are identified as open, the line is determined in outage condition.
The FIS is simulated in MATLAB fuzzy toolbox. The components of the FIS are membership function, rule base
and fuzzy designer which are described in the following.
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4.2.1 Membership Function
The proposed fuzzy multi line outage detection system includes inputs which are the lines CBs status and outputs
which are the status of the scenario. For both input and output it is needed to define membership functions. The line
CBs status namely LBK-ST is the FIS input and consists of triangular membership functions. Also, the LBK-ST has
Tripped (0) and Not-Tripped (1) membership functions. The range of Tripped and Not-Tripped membership
functions are (1, 0, 1) and (0, 1, 2) respectively. When the line CBs are open, membership function is considered as
Tripped which related to outage of line. Also, for closed position of the line CBs, membership function is considered
as Not-Tripped which is related to normal status of line and means the power is passing through the line. The output
of the proposed FIS is SCENARIO-INDEX and indicates the index of the predefined line outage scenarios. Also, the
SCENARIO-INDEX is a constant membership function[17].
4.2.2 Rule Base
The most important part of the proposed fuzzy system is the line outage scenario mechanism. For this purpose, the
fuzzy rules are used for single line and multi-line outage scenarios. Therefore, based on the status of predefined
membership functions, the rules are formulated. Table 1 shows the rules used in the proposed fuzzy-multi line
outage detection system which already has applied to IEEE 5-bus system.
The special lettersT and NTin Table 1 represents the Tripped and Not-Tripped status of the line CBs. As it can be
seen from Table 1, each scenario is corresponded to one network topology including single line or multi line
outage[18]. Using the signals obtained from the CBs by power flow meters, the status of the lines is detected and the
index of related predefined scenario is identified by applying the proposed fuzzy line outage detection system.
Table 1: Fuzzy Rules bus system
Scenario
Index
Lines CBs status
Scenario Status
1-2
2-3
2-4
2-5
3-4
4-5
1
T
NT
NT
NT
NT
NT
Line 1-2 outage
2
NT
NT
NT
NT
NT
NT
Line 1-3 outage
3
NT
T
NT
NT
NT
NT
Line 2-3 outage
4
NT
NT
T
NT
NT
NT
Line 2-4 outage
5
NT
NT
NT
T
NT
NT
Line 2-5 outage
6
NT
NT
NT
NT
T
NT
Line 3-4 outage
7
NT
NT
NT
NT
NT
T
Line 4-5 outage
8
T
T
NT
NT
NT
NT
Line1-2 and 2-3
outage
9
T
NT
NT
NT
NT
NT
Line1-2 and 1-3
outage
10
T
NT
T
NT
NT
NT
Line1-2 and 2-4
outage
11
T
NT
NT
T
NT
NT
Line1-2 and 2-5
outage
12
T
NT
NT
NT
T
NT
Line1-2 and 3-4
outage
13
T
NT
NT
NT
NT
T
Line1-2 and 4-5
outage
14
NT
T
NT
NT
NT
NT
Line1-3 and 2-3
outage
15
NT
NT
T
NT
NT
NT
Line1-3 and 2-4
outage
16
NT
NT
NT
T
NT
NT
Line1-3 and 2-5
outage
17
NT
NT
NT
NT
T
NT
Line1-3 and 3-4
outage
18
NT
NT
NT
NT
NT
T
Line1-3 and 4-5
outage
19
NT
T
T
NT
NT
NT
Line2-3 and 2-4
outage
20
NT
T
NT
T
NT
NT
Line2-3 and 2-5
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4.2.3 Fuzzy Logic Designer
A fuzzylogicdesigner is used to design and test the FIS for modeling the system behaviors. The fuzzy logic designer
which used for multi-line outage detection is consist of the status of lines CBs as inputs and the index of predefined
line outage scenario as output. This is done by de-fuzzification of the membership functions. The fuzzy logic
designer for multi-line outage detection is shown in figure 6.
Figure 6. Fuzzy logic designer for line outage detection
5.SIMULATION RESULTS
The proposed FIS identifying themulti-line outage scenarios is tested on IEEE-5 bus system. It consists of 2
generators, 3 load buses and 7 lines. Based on the predefined rules given in Table 1, the output which is the index
value of predefined scenario has been selected. Finally, the selection scenariosand the outcome of the various profile
for our proposed Fuzzy based multi-line Power Outage Control Systemare shown in figures 7-10.
outage
21
NT
T
NT
NT
T
NT
Line2-3 and 3-4
outage
22
NT
T
NT
NT
NT
T
Line2-3 and 4-5
outage
23
NT
NT
T
T
NT
NT
Line2-4 and 2-5
outage
24
NT
NT
T
NT
T
NT
Line2-4 and 3-4
outage
25
NT
NT
T
NT
NT
T
Line2-4 and 4-5
outage
26
NT
NT
NT
T
T
NT
Line2-5 and 3-4
outage
27
NT
NT
NT
T
NT
T
Line2-5 and 4-5
outage
28
NT
NT
NT
NT
T
T
Line3-4 and 4-5
outage
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Figure 7. Voltage magnitudes
Figure 8. Voltage phase angle
Figure 9. Active power
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Figure 10. Reactive power
The outcome of all these figure presages that the overall network is balanced. The result statistics has determined the
voltage and the currents at all the buses. However,it also showed that phases have equal in magnitude and shifted by
120o with respect to the each other. Nonetheless, the results have been verified in the current scenarios. The powers
at the buses presages total power pass through the connected transmission lines, thus the capability of the bus can be
assessed. All the results are tabulated without loads and with loads are shown in TABLE 1. it is very useful to
examine the parameters change at all the lines from no-load case to Full load case. The results acquired after this
experiment are useful to presage the performance of model with the actual power system.
6.CONCLUSION
The fuzzy based multi-line Power Outage Control System was one of the best outcome performance measuring
mechanism for the power system. The line CBs status are considered as an index for line outage purpose.The line is
determined in outage condition when the CBs are open. A fuzzy system is used to detect the predefined line outage
scenario based on the lines CBs status. The advantage of the proposed system has proved that there is no need the
information about the lines phase angles which remove the drawback of complexity. Also, the proposed fuzzy
system can be extended for wide grids to detect more line outage contingencies by definition additional fuzzy rules
and can be used as a simple comprehensive GUIfor simple and fast decision making. The identification of multi-line
outage scenarios in the power system are useful for control and protection purposes. The proposed method is applied
to IEEE 5-bus system and the results show its efficiency in detection of line outage scenarios.
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... This method did not elicit statistics on the social distance among the patient population, and as a result, it is concerned about the sensitivity issue.Development of shrewd modelMultiple datasets from various remote places are gathered to develop the shrewd model. The gathered data is constantly being updated with new data from the fi eld[29][30][31][32][33][34][35][36][37][38][39]. Thesuggested shrewd model takes into account the risk of contact transmission, the average frequency of encounters between persons as a function of time, and the amount of time a person remains infected and hence can infect others fi nally it will give a fruitful result about the people have directed infected.Figure 1, is depicting how aggravatedly the COVID-19 pandemic is propagating. ...
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... This method did not elicit statistics on the social distance among the patient population, and as a result, it is concerned about the sensitivity issue.Development of shrewd modelMultiple datasets from various remote places are gathered to develop the shrewd model. The gathered data is constantly being updated with new data from the fi eld[29][30][31][32][33][34][35][36][37][38][39]. Thesuggested shrewd model takes into account the risk of contact transmission, the average frequency of encounters between persons as a function of time, and the amount of time a person remains infected and hence can infect others fi nally it will give a fruitful result about the people have directed infected.Figure 1, is depicting how aggravatedly the COVID-19 pandemic is propagating. ...
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The unavoidable pandemic situation seems uncontrolled over past two years. In this aggravated situation, it seems crucial taking inescapable measures to deal with such defiant disease and it is only possible if the actual pandemic propagation ratio is to be acquainted. Therefore, a shrewd pandemic model is being developed that will generate real-time infection statistics on a hourly, weekly, and monthly basis. This shrewd model utilizes the well-known dataset and when this dataset will be applied to determine the status of three types of infection as the number of infected people, the time when pandemic begins, and the time when the pandemic disappears. The time-based results are generated by conducting simulation in python Simpy framework and the generated results are the hallmark of real-time infection spreading ratio and it shows that when the extraordinary measures for infection ratio are indispensable and when it becomes plausible.
... where A and B are fuzzy sets, and z is a polynomial or a constant (Emad Hussen et al. 2020). Hence, a neuro-fuzzy model can incorporate human knowledge and self-learning competencies that can potentially approximate every situation (Mohd Salleh and Hussain 2016). ...
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