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ScienceDirect
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Procedia Computer Science 167 (2020) 1148–1160
1877-0509 © 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Data
Science (ICCIDS 2019).
10.1016/j.procs.2020.03.417
10.1016/j.procs.2020.03.417 1877-0509
© 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientic committee of the International Conference on Computational Intelligence and Data
Science (ICCIDS 2019).
Available online at www.sciencedirect.com
ScienceDirect
Procedia Computer Science 00 (2019) 000–000
www.elsevier.com/locate/procedia
1877-0509 © 2019 The Authors. Published by Elsevier B.V.. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Data Science
(ICCIDS 2019)
International Conference on Computational Intelligence and Data Science (ICCIDS 2019)
IoT Based Wireless Sensor Network for Power Quality Control in
Smart Grid
Amam Hossain Bagdadee1*,Md Zahirul Hoque2,Li Zhang1
College of Energy and Electrical Engineering, Hohai University, Nanjing, 210098, China 1
College of Computer and Information, Hohai University. Nanjing, 210098, China2
Abstract
The IoT based Power management system requires data from the feeder in the grid. Sufficient power supply with
demand is a significant challenge for several countries around the world. Rapid growing demand for power supply
requires power quality enhancement to get higher reliability in the smart grid. This smart power system sensor
equipped that measures grid power capacity and update to the organization on a consistent schedule. Energy
supplies to the specific region indicated by power install capacity in the grid; use a global system for mobile
communications (GSM) messaging service to notify customers of power generation and power supply time. The IoT
based wireless Sensor network (WSN) is a revolutionary system for smart monitoring. In this article propose a
system demonstrate for the progress and implementation of WSN-based communication systems for smart
monitoring and automated control in the electric grid. This work allows for the improvement of grid sharing for
maintaining power quality. The dynamic controller has controlled the event of Power quality problem and voltage
rise. Appropriate systems and controllers have been demonstrated and analyzed for control performance of a
monitoring system in the smart grid.
© 2019 The Authors. Published by Elsevier B.V.. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and
Data Science (ICCIDS 2019)
Keywords: Internet of Things (IoT), WSN, GSM, Active Power Control, Communication System, Smart meter, Smart Grid
* Corresponding author. Tel.: +8615951723758;
E-mail address: a.bagdadee@hhu .edu.cn
Available online at www.sciencedirect.com
ScienceDirect
Procedia Computer Science 00 (2019) 000–000
www.elsevier.com/locate/procedia
1877-0509 © 2019 The Authors. Published by Elsevier B.V.. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Data Science
(ICCIDS 2019)
International Conference on Computational Intelligence and Data Science (ICCIDS 2019)
IoT Based Wireless Sensor Network for Power Quality Control in
Smart Grid
Amam Hossain Bagdadee1*,Md Zahirul Hoque2,Li Zhang1
College of Energy and Electrical Engineering, Hohai University, Nanjing, 210098, China 1
College of Computer and Information, Hohai University. Nanjing, 210098, China2
Abstract
The IoT based Power management system requires data from the feeder in the grid. Sufficient power supply with
demand is a significant challenge for several countries around the world. Rapid growing demand for power supply
requires power quality enhancement to get higher reliability in the smart grid. This smart power system sensor
equipped that measures grid power capacity and update to the organization on a consistent schedule. Energy
supplies to the specific region indicated by power install capacity in the grid; use a global system for mobile
communications (GSM) messaging service to notify customers of power generation and power supply time. The IoT
based wireless Sensor network (WSN) is a revolutionary system for smart monitoring. In this article propose a
system demonstrate for the progress and implementation of WSN-based communication systems for smart
monitoring and automated control in the electric grid. This work allows for the improvement of grid sharing for
maintaining power quality. The dynamic controller has controlled the event of Power quality problem and voltage
rise. Appropriate systems and controllers have been demonstrated and analyzed for control performance of a
monitoring system in the smart grid.
© 2019 The Authors. Published by Elsevier B.V.. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and
Data Science (ICCIDS 2019)
Keywords: Internet of Things (IoT), WSN, GSM, Active Power Control, Communication System, Smart meter, Smart Grid
* Corresponding author. Tel.: +8615951723758;
E-mail address: a.bagdadee@hhu .edu.cn
2 Amam Hossain et al./ Procedia Computer Science 00 (2019) 000–000
1. Introduction
IoT is a significant regional arrangement that is related to the typical features of a conventional system that can
communicate and trade data with one another. The system method can include any equipment, programming or
sensors. IoT provides data management and security management. IoT connects individuals and objects from
anywhere [1]. IoT can use in a combination of uses such as vehicle response to vehicles, smart buildings, acute
stress, quick medical services, smart cities [2]. The internet is currently a series of system associations where the
number of related devices is overgrowing. At present, the internet is being used to access process and order ongoing
parameters from remote locations. A large number of sensors are used to control electrical machines for a long time
in the past few days for domestic automation. However, it is not appropriately implemented, then cost-effectiveness
and efficiency will not improve [3]. For home automation, a large number of sensors have been used for a long time
to control electrical equipment. This is not cost effective because of the many sensors used. Each device requires its
sensor, so the cost and power consumed will increase as the number of devices increases. In modern IoT systems, a
large number of sensors can replace by a small number of sensors, IoT can be placed on one platform and thus
consume power and energy. The context recognition system is designed to operate IoT effectively. Even in the most
potent scenarios, wireless sensor networks that play an essential role in various monitoring applications are ideal
application [4]. The emergence of a smarter grid increases the reliability of the system by taking pro-active steps
when the power crisis and natural disasters occur. Increased emissions make it easier for consumers to reduce their
dependence on the grid which involves greenhouse gas emissions from burning fossil fuels. Distributed generation
using electronic converters and inverters, it is possible to overcome distributed problems by activating grid and
island mode failures to cause power plants to turn off [5]. Detecting and control framework has three primary stages
in particular: sensing stage, data response stage, and control stage. The sensing unit must generally operate by using
a wireless sensor node (WSN) [6]. It is estimated that the WSN is exceptional and not suitable for a variety of
topologies, is a versatile and promising innovation, allowing proper inspection and enhancement of power system
[7]. Data correspondence can realize with the ultra-low power RF (radio frequency) signal used by the WSN
receiver module. The control framework can appreciate with an electronic power converter that used as a substitute
to send the generated control to the network.
This framework provides data to headquarters using a web server for database support. The database protected by
ensuring that the secret key is password. The consumer is notified to pay every level of utilization of the control.
Input power measured in number fractions such as units per minute. The power unit estimated with a power flow
sensor related to Arduino [8]. The remainder of this paper as followed: the central segment can solve the problems
that distinguished by clarifying indigenous techniques.
2. Literature
In literature, Smart sensors can be considered the essential IoT devices for smart grids. The smart sensor is the
device that informs control systems about specific parameters and what happens to actual substance monitored.
Smart sensors provide rough information to handle information, detailed analysis [9]. Nowadays, innovations in
several advanced sensors are connected in various fields. Principal objectives are to make technical arrangements to
achieve the accuracy of unusual situations and improve system quality and reliability. IoT shows intelligent devices
in a power system that are intelligently related to information collected from installed sensors, actuators, and other
physical documents [10]. In future IoT needs to increase customer satisfaction and business efficiency and
immediately distribute other administrative elements that open opportunities [11]. Another vital part of the IoT
framework is the adjustment of various seasons [14]. The IoT-based framework must have the ability to handle and
change in response to these changes that can always apply the IoT framework right at that time. In this way, an
essential part of the IoT-based framework, care for conventional varieties ends. Also, physical parameters from
various locales are also perfect[15]. This system consists of two units such as director of resources and autonomy
[16]. The resources monitored are essential substances, and it consists of sensors and effectors. The sensor detects
nature and collects information. Sensor detectors are an interface that is used by the intelligent devices to control the
earth [17]. Autonomous heads are also more complicated to provide embedded controls and conduct information
Amam Hossain Bagdadee et al. / Procedia Computer Science 167 (2020) 1148–1160 1149
Available online at www.sciencedirect.com
ScienceDirect
Procedia Computer Science 00 (2019) 000–000
www.elsevier.com/locate/procedia
1877-0509 © 2019 The Authors. Published by Elsevier B.V.. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Data Science
(ICCIDS 2019)
International Conference on Computational Intelligence and Data Science (ICCIDS 2019)
IoT Based Wireless Sensor Network for Power Quality Control in
Smart Grid
Amam Hossain Bagdadee1*,Md Zahirul Hoque2,Li Zhang1
College of Energy and Electrical Engineering, Hohai University, Nanjing, 210098, China 1
College of Computer and Information, Hohai University. Nanjing, 210098, China2
Abstract
The IoT based Power management system requires data from the feeder in the grid. Sufficient power supply with
demand is a significant challenge for several countries around the world. Rapid growing demand for power supply
requires power quality enhancement to get higher reliability in the smart grid. This smart power system sensor
equipped that measures grid power capacity and update to the organization on a consistent schedule. Energy
supplies to the specific region indicated by power install capacity in the grid; use a global system for mobile
communications (GSM) messaging service to notify customers of power generation and power supply time. The IoT
based wireless Sensor network (WSN) is a revolutionary system for smart monitoring. In this article propose a
system demonstrate for the progress and implementation of WSN-based communication systems for smart
monitoring and automated control in the electric grid. This work allows for the improvement of grid sharing for
maintaining power quality. The dynamic controller has controlled the event of Power quality problem and voltage
rise. Appropriate systems and controllers have been demonstrated and analyzed for control performance of a
monitoring system in the smart grid.
© 2019 The Authors. Published by Elsevier B.V.. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and
Data Science (ICCIDS 2019)
Keywords: Internet of Things (IoT), WSN, GSM, Active Power Control, Communication System, Smart meter, Smart Grid
* Corresponding author. Tel.: +8615951723758;
E-mail address: a.bagdadee@hhu .edu.cn
Available online at www.sciencedirect.com
ScienceDirect
Procedia Computer Science 00 (2019) 000–000
www.elsevier.com/locate/procedia
1877-0509 © 2019 The Authors. Published by Elsevier B.V.. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Data Science
(ICCIDS 2019)
International Conference on Computational Intelligence and Data Science (ICCIDS 2019)
IoT Based Wireless Sensor Network for Power Quality Control in
Smart Grid
Amam Hossain Bagdadee1*,Md Zahirul Hoque2,Li Zhang1
College of Energy and Electrical Engineering, Hohai University, Nanjing, 210098, China 1
College of Computer and Information, Hohai University. Nanjing, 210098, China2
Abstract
The IoT based Power management system requires data from the feeder in the grid. Sufficient power supply with
demand is a significant challenge for several countries around the world. Rapid growing demand for power supply
requires power quality enhancement to get higher reliability in the smart grid. This smart power system sensor
equipped that measures grid power capacity and update to the organization on a consistent schedule. Energy
supplies to the specific region indicated by power install capacity in the grid; use a global system for mobile
communications (GSM) messaging service to notify customers of power generation and power supply time. The IoT
based wireless Sensor network (WSN) is a revolutionary system for smart monitoring. In this article propose a
system demonstrate for the progress and implementation of WSN-based communication systems for smart
monitoring and automated control in the electric grid. This work allows for the improvement of grid sharing for
maintaining power quality. The dynamic controller has controlled the event of Power quality problem and voltage
rise. Appropriate systems and controllers have been demonstrated and analyzed for control performance of a
monitoring system in the smart grid.
© 2019 The Authors. Published by Elsevier B.V.. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and
Data Science (ICCIDS 2019)
Keywords: Internet of Things (IoT), WSN, GSM, Active Power Control, Communication System, Smart meter, Smart Grid
* Corresponding author. Tel.: +8615951723758;
E-mail address: a.bagdadee@hhu .edu.cn
2 Amam Hossain et al./ Procedia Computer Science 00 (2019) 000–000
1. Introduction
IoT is a significant regional arrangement that is related to the typical features of a conventional system that can
communicate and trade data with one another. The system method can include any equipment, programming or
sensors. IoT provides data management and security management. IoT connects individuals and objects from
anywhere [1]. IoT can use in a combination of uses such as vehicle response to vehicles, smart buildings, acute
stress, quick medical services, smart cities [2]. The internet is currently a series of system associations where the
number of related devices is overgrowing. At present, the internet is being used to access process and order ongoing
parameters from remote locations. A large number of sensors are used to control electrical machines for a long time
in the past few days for domestic automation. However, it is not appropriately implemented, then cost-effectiveness
and efficiency will not improve [3]. For home automation, a large number of sensors have been used for a long time
to control electrical equipment. This is not cost effective because of the many sensors used. Each device requires its
sensor, so the cost and power consumed will increase as the number of devices increases. In modern IoT systems, a
large number of sensors can replace by a small number of sensors, IoT can be placed on one platform and thus
consume power and energy. The context recognition system is designed to operate IoT effectively. Even in the most
potent scenarios, wireless sensor networks that play an essential role in various monitoring applications are ideal
application [4]. The emergence of a smarter grid increases the reliability of the system by taking pro-active steps
when the power crisis and natural disasters occur. Increased emissions make it easier for consumers to reduce their
dependence on the grid which involves greenhouse gas emissions from burning fossil fuels. Distributed generation
using electronic converters and inverters, it is possible to overcome distributed problems by activating grid and
island mode failures to cause power plants to turn off [5]. Detecting and control framework has three primary stages
in particular: sensing stage, data response stage, and control stage. The sensing unit must generally operate by using
a wireless sensor node (WSN) [6]. It is estimated that the WSN is exceptional and not suitable for a variety of
topologies, is a versatile and promising innovation, allowing proper inspection and enhancement of power system
[7]. Data correspondence can realize with the ultra-low power RF (radio frequency) signal used by the WSN
receiver module. The control framework can appreciate with an electronic power converter that used as a substitute
to send the generated control to the network.
This framework provides data to headquarters using a web server for database support. The database protected by
ensuring that the secret key is password. The consumer is notified to pay every level of utilization of the control.
Input power measured in number fractions such as units per minute. The power unit estimated with a power flow
sensor related to Arduino [8]. The remainder of this paper as followed: the central segment can solve the problems
that distinguished by clarifying indigenous techniques.
2. Literature
In literature, Smart sensors can be considered the essential IoT devices for smart grids. The smart sensor is the
device that informs control systems about specific parameters and what happens to actual substance monitored.
Smart sensors provide rough information to handle information, detailed analysis [9]. Nowadays, innovations in
several advanced sensors are connected in various fields. Principal objectives are to make technical arrangements to
achieve the accuracy of unusual situations and improve system quality and reliability. IoT shows intelligent devices
in a power system that are intelligently related to information collected from installed sensors, actuators, and other
physical documents [10]. In future IoT needs to increase customer satisfaction and business efficiency and
immediately distribute other administrative elements that open opportunities [11]. Another vital part of the IoT
framework is the adjustment of various seasons [14]. The IoT-based framework must have the ability to handle and
change in response to these changes that can always apply the IoT framework right at that time. In this way, an
essential part of the IoT-based framework, care for conventional varieties ends. Also, physical parameters from
various locales are also perfect[15]. This system consists of two units such as director of resources and autonomy
[16]. The resources monitored are essential substances, and it consists of sensors and effectors. The sensor detects
nature and collects information. Sensor detectors are an interface that is used by the intelligent devices to control the
earth [17]. Autonomous heads are also more complicated to provide embedded controls and conduct information
1150 Amam Hossain Bagdadee et al. / Procedia Computer Science 167 (2020) 1148–1160
Amam Hossain et al / Procedia Computer Science 00 (2019) 000–000 3
investigations. Controls collected combine observation and disclosure of information [18]. Observed domains and
information collected. Information is separated, monitored, and then revealed. The inspection department models
the environment and learns about the earth. This will help to predict future situations. In transmission and
distribution monitoring systems, smart sensors can be used in various places that can be distributed to networks such
as electricity networks, distribution lines, distribution transformers, substations. They are two types of smart sensors
that can be used [19]. The main types are used to measure climate conditions around advantages such as wind
direction sensors, temperature sensors, wind speed sensors, sticky sensors, rain sensors, and so on. These sensors are
responsible for assistance and mitigation for violent incidents that cannot be accessed with the current network. In
this way, it becomes a structure of autonomous learning. This framework will be more capable and reliable for
improving power system.
3. Problem Formulation
This problem selects the optimal location of the capability and the most constructive bulk of the distributed
generation unit. The initial stage of this technique is to characterize the objective. It reveals multi-objective
operation based on the power quality problem and a voltage stability indices:
The power quality indices =
L LDG
L
EE
EPI E
(1)
EL is actual power loss with no compensation; the ELDG is a not actual power loss after the expansion of DG. The
voltage stability indices that effectively combine the effects of actual and apparent reactive power growth
scientifically details as follows.
22
2
1
2
2 Cos( ) 2 Sin( )
11 11
1
MPQ
xx
N
VMM
KPQ
XX
NN N
Vs
(2)
When VS1 is called the voltage stability index of the value Px and Qx are the real power and reactive power at the
receiving end. VK is the amount of voltage on the transmitter side. M<
1
and N <
1
are limits on the
transmission line. As long as the high index is less than 1, the system is stable. However, it will be put together at
the system. Objective functions are regulated considering power quality problem and voltage stability,
respectively.
2
1
1
min
2,............,
s
i
F IS
is
(3)
2
1
max
1
2,............,
F
VS
is
(4)
Where, F1 and F2 are aspects that should consider. The actual operation is responsible for standard output power
regulated for impartiality criteria and the other imbalance requirements. The magnitude of the transport voltage, the
actual and apparent output power limits are flowing:
min max
I II
V VV (5)
4 Amam Hossain et al./ Procedia Computer Science 00 (2019) 000–000
min max
I II
P PP (6)
min max
I II
Q QQ (7)
4. System Design
Major equipment:
This system was based on Arduino, remote sensors, ultrasonic sensors, GSM modules. This system can measure
power capacity and provide measured data to local offices.
A. Arduino
Arduino is a PC device and programming organization, business entity, consumer network that builds and creates
microcontroller units to create sophisticated devices and smart substance that can detect and control substance in the
system [20]. This framework provides a digital and comfortable set of information/results (I/O) sticks that can
interact with different extension panel ("shields") and circuits.
B. Power measurement theory
The apparent power is the voltage of an alternating current (AC) framework multiplied with each current that
streams into it. It registered as the result of (Root Mean Square)RMS voltage value and RMS current, as appeared in
Eq. (1) Moreover, it is communicated in units of voltage/volt-amperes(V/A). Current RMS value and Voltage RMS
value esteems are computed utilizing Eq.(2) and (3) where N is the number of analysis and i(s), and u(s) are the
examples of the electrical current and voltage signals.
Where,
RMS RMS
ApparantPower Current Voltage
(8)
1
2
0
S
RMS
s
Current i S
(9)
1
2
0
S
RMS
s
Voltage u S
(10)
Furthermore, the actual power supply is the circuit operating at once. This must be confirmed simultaneously by
estimating voltage and current, parallel and averaging for a while:
!
0
() ()
S
s
ActualPower i S u S
(11)
The fraction of real power to apparent power is called the power factor (equation (12) and refers to the capability of
the electrical system in a facility to convert the current workload to useful such as heat or light.
/
PowerFactor ActualPower ApperentPower
(12)
The actual power estimated by calculating the apparent power, because of the non-linear load that breaks the wave,
or because the energy is put back into the load. There is always a current that can draw from the source. In such
Amam Hossain Bagdadee et al. / Procedia Computer Science 167 (2020) 1148–1160 1151
Amam Hossain et al / Procedia Computer Science 00 (2019) 000–000 3
investigations. Controls collected combine observation and disclosure of information [18]. Observed domains and
information collected. Information is separated, monitored, and then revealed. The inspection department models
the environment and learns about the earth. This will help to predict future situations. In transmission and
distribution monitoring systems, smart sensors can be used in various places that can be distributed to networks such
as electricity networks, distribution lines, distribution transformers, substations. They are two types of smart sensors
that can be used [19]. The main types are used to measure climate conditions around advantages such as wind
direction sensors, temperature sensors, wind speed sensors, sticky sensors, rain sensors, and so on. These sensors are
responsible for assistance and mitigation for violent incidents that cannot be accessed with the current network. In
this way, it becomes a structure of autonomous learning. This framework will be more capable and reliable for
improving power system.
3. Problem Formulation
This problem selects the optimal location of the capability and the most constructive bulk of the distributed
generation unit. The initial stage of this technique is to characterize the objective. It reveals multi-objective
operation based on the power quality problem and a voltage stability indices:
The power quality indices =
L LDG
L
EE
EPI E
(1)
EL is actual power loss with no compensation; the ELDG is a not actual power loss after the expansion of DG. The
voltage stability indices that effectively combine the effects of actual and apparent reactive power growth
scientifically details as follows.
22
2
1
2
2 Cos( ) 2 Sin( )
11 11
1
MPQ
xx
N
VMM
KPQ
XX
NN N
Vs
(2)
When VS1 is called the voltage stability index of the value Px and Qx are the real power and reactive power at the
receiving end. VK is the amount of voltage on the transmitter side. M<
1
and N <
1
are limits on the
transmission line. As long as the high index is less than 1, the system is stable. However, it will be put together at
the system. Objective functions are regulated considering power quality problem and voltage stability,
respectively.
2
1
1
min
2,............,
s
i
F IS
is
(3)
2
1
max
1
2,............,
F
VS
is
(4)
Where, F1 and F2 are aspects that should consider. The actual operation is responsible for standard output power
regulated for impartiality criteria and the other imbalance requirements. The magnitude of the transport voltage, the
actual and apparent output power limits are flowing:
min max
I II
V VV (5)
4 Amam Hossain et al./ Procedia Computer Science 00 (2019) 000–000
min max
I II
P PP (6)
min max
I II
Q QQ (7)
4. System Design
Major equipment:
This system was based on Arduino, remote sensors, ultrasonic sensors, GSM modules. This system can measure
power capacity and provide measured data to local offices.
A. Arduino
Arduino is a PC device and programming organization, business entity, consumer network that builds and creates
microcontroller units to create sophisticated devices and smart substance that can detect and control substance in the
system [20]. This framework provides a digital and comfortable set of information/results (I/O) sticks that can
interact with different extension panel ("shields") and circuits.
B. Power measurement theory
The apparent power is the voltage of an alternating current (AC) framework multiplied with each current that
streams into it. It registered as the result of (Root Mean Square)RMS voltage value and RMS current, as appeared in
Eq. (1) Moreover, it is communicated in units of voltage/volt-amperes(V/A). Current RMS value and Voltage RMS
value esteems are computed utilizing Eq.(2) and (3) where N is the number of analysis and i(s), and u(s) are the
examples of the electrical current and voltage signals.
Where,
RMS RMS
ApparantPower Current Voltage
(8)
1
2
0
S
RMS
s
Current i S
(9)
1
2
0
S
RMS
s
Voltage u S
(10)
Furthermore, the actual power supply is the circuit operating at once. This must be confirmed simultaneously by
estimating voltage and current, parallel and averaging for a while:
!
0
() ()
S
s
ActualPower i S u S
(11)
The fraction of real power to apparent power is called the power factor (equation (12) and refers to the capability of
the electrical system in a facility to convert the current workload to useful such as heat or light.
/
PowerFactor ActualPower ApperentPower
(12)
The actual power estimated by calculating the apparent power, because of the non-linear load that breaks the wave,
or because the energy is put back into the load. There is always a current that can draw from the source. In such
1152 Amam Hossain Bagdadee et al. / Procedia Computer Science 167 (2020) 1148–1160
Amam Hossain et al / Procedia Computer Science 00 (2019) 000–000 5
cases, the power factor is not 100%. As a general rule, apparent power can consider as a limit on actual power.
C. Power Sensor
Structurally, its use is SCT - 013 - 030 current sensors which can recognize the importance of IAC from 0 to 30 A.
The current level of induction sensor is N = 1800, and the result is relative V. As shown in equation (6), I work from
1V to 62O when checking obstacles. This sensor selected for extended work, accuracy, and comfort that can be
accessed by Open Energy Monitor. The sensor output signal is prepared with a time window of 10 AC signal cycles
to ensure the correct Current RMS usual incentive. Given European standards, all electricity companies have a
recurrence of 50 Hz, and an inspection window of around 200 ms is taken to maintain a strategic distance with
nominal plume based on the Nyquist Shannon hypothesis test. However, based on 250 milliseconds or less window
use trials, especially, comparing product results with industrially accessible products, or estimating devices, that is
entirely accurate. Regardless of the device sensor, use a low-passing programming channel from the flag that is
checked to issue a stable balance that present, test the negative estimate of the flag, and confirm the respect of the
Current RMS.
The Voltage RMS is stable from specific electrical systems (around 230V an in the EU). There is no doubt that this
frustrates the general accuracy of estimates. In this method, that will improve procedures to build a framework and
reduce the cost of the establishment of these lines. Even more critical is to critically limit all possible problems
caused by gadget activity (such as device disappointments) and not affect the security of the general framework.
D. Power quality with DMR meters
The DMR meters introduced at the consumer association focus that can track the power quality of the low voltage
system. The structure of DMR metering can measure power quality circumstance, for instance, measurements of
power consumption, common conductor defects, excessive phase sequences, over current, isolated, termination of
the power supply and grid inversion that can measure the supply of the distributed network. During PV generation
determines voltage deviations that especially unapproved microgeneration and over and under voltages is essential.
Because of the nature of the measurement and implementation of communication technologies, the estimated quality
of the power achieved should not be continuous. Evaluation of power quality can reflect in initial estimates. This is
because the measurement data is updated to the Caruana’s data framework every day at midnight. The general
process structure for DMR meter data collection demonstrated in Fig. Individual DMR meters assemble utilization
and power quality information into the internal memory in the packets including the 4-hour estimate. When the
packets of a particular meter end, the meter focus the information concentrator and the concentrator restores the
packet from the meter. Information concentrators usually located in optional substances from the LV settings. The
communication between the DMR meter and concentrator uses power lines communication (PLC), which means
that this signal transmitted through the current LV allocation settings. In this way, because the signal cannot transmit
through a current transformer, the information collection device must be associated with the LV setting that is
equivalent to the DMR meter. Relevant information will synchronize with the distributed system meter management
framework such as working hours of information gathering equipment within hour’s afternoon. The connection
between the information concentrator and the meter management framework is terminated using a conventional 2G
or 3G portable system from the measurement management framework, quality control and utilization of information
circulated to the desired client application framework. The 24-hour synchronization cycle means that opportunity
information that can be accessed by the system operator must reflect from the previous time. However, several
opportunities can distinguish as opportunities with high needs. If there is such an opportunity, the DMR meter will
send a quick sign to the concentrator, restore it instantly and communicate information to the meter management
framework. Under the situation of the Caruana’s conductor and deficiencies in the performing arts named
opportunities with high needs power Line Connection in general that can consider as particular strategies where
moods have changed, and after a while, it cannot configure the clutch interface between the central unit and the
instrument. As a result, DMR meters are ready to store various packets in internal memory too.
6 Amam Hossain et al./ Procedia Computer Science 00 (2019) 000–000
Fig.1.Digital metering infrastructure
The length of the package that can store in the memory meter changes from different weeks to different months
depending on the purpose of the estimate and the estimated number of channels used. In this way, it can imagine that
the delay in high requirements for the warning framework will extend. In this case, the adequacy of the estimated
DMR meter data in the first grid operational management, which reaction time must be fast in principle, can be
compromised. Even though the situation is high, that can physically investigate the estimated data from each DMR
meter. An application can send from the measurement management framework or various frameworks set for it. The
useful association results to the DMR meter send the current estimated data to the framework. In both cases, because
of the vulnerability of tasks that meet power line connection requirements, the relationship with the condition of the
meter can be framed, and the information concentrator can stop the request. In this situation, that cannot obtain
current continuous data from the instrument, and the last 4 hours estimated package that can be accessed by the
concentrator collected.DMR meters offer broad prospects for LV power quality opportunities and reflect a 24-hour
delay. Because the entrance to the meter foundation is high, it is possible to form detailed images of past events in
the grid effectively. Also, opportunities in the sample zone, in particular, can be checked by the time of the
inspection, using manual survey usability from the meter management framework. For example, DMS programming
used through Caruana is built into the meter management framework and makes it possible to survey opportunity
data on the quality intensity of the desired area. Also, the current opportunity level data is sufficient to achieve a
general view of power quality issues, presenting tighter opportunities requiring the handling and separation of
information obtained within the framework of meter management. The Generation of DMR meters is currently best
used for reflecting different power quality problems, thus identifying LV systems with, for example, apparent and
intermittent power quality problems. Due to the lack of continuing information, DMR meter inventory is difficult to
use with primary control or voltage control applications in progress and operation.
Fig.2. System architecture Diagram
Amam Hossain Bagdadee et al. / Procedia Computer Science 167 (2020) 1148–1160 1153
Amam Hossain et al / Procedia Computer Science 00 (2019) 000–000 5
cases, the power factor is not 100%. As a general rule, apparent power can consider as a limit on actual power.
C. Power Sensor
Structurally, its use is SCT - 013 - 030 current sensors which can recognize the importance of IAC from 0 to 30 A.
The current level of induction sensor is N = 1800, and the result is relative V. As shown in equation (6), I work from
1V to 62O when checking obstacles. This sensor selected for extended work, accuracy, and comfort that can be
accessed by Open Energy Monitor. The sensor output signal is prepared with a time window of 10 AC signal cycles
to ensure the correct Current RMS usual incentive. Given European standards, all electricity companies have a
recurrence of 50 Hz, and an inspection window of around 200 ms is taken to maintain a strategic distance with
nominal plume based on the Nyquist Shannon hypothesis test. However, based on 250 milliseconds or less window
use trials, especially, comparing product results with industrially accessible products, or estimating devices, that is
entirely accurate. Regardless of the device sensor, use a low-passing programming channel from the flag that is
checked to issue a stable balance that present, test the negative estimate of the flag, and confirm the respect of the
Current RMS.
The Voltage RMS is stable from specific electrical systems (around 230V an in the EU). There is no doubt that this
frustrates the general accuracy of estimates. In this method, that will improve procedures to build a framework and
reduce the cost of the establishment of these lines. Even more critical is to critically limit all possible problems
caused by gadget activity (such as device disappointments) and not affect the security of the general framework.
D. Power quality with DMR meters
The DMR meters introduced at the consumer association focus that can track the power quality of the low voltage
system. The structure of DMR metering can measure power quality circumstance, for instance, measurements of
power consumption, common conductor defects, excessive phase sequences, over current, isolated, termination of
the power supply and grid inversion that can measure the supply of the distributed network. During PV generation
determines voltage deviations that especially unapproved microgeneration and over and under voltages is essential.
Because of the nature of the measurement and implementation of communication technologies, the estimated quality
of the power achieved should not be continuous. Evaluation of power quality can reflect in initial estimates. This is
because the measurement data is updated to the Caruana’s data framework every day at midnight. The general
process structure for DMR meter data collection demonstrated in Fig. Individual DMR meters assemble utilization
and power quality information into the internal memory in the packets including the 4-hour estimate. When the
packets of a particular meter end, the meter focus the information concentrator and the concentrator restores the
packet from the meter. Information concentrators usually located in optional substances from the LV settings. The
communication between the DMR meter and concentrator uses power lines communication (PLC), which means
that this signal transmitted through the current LV allocation settings. In this way, because the signal cannot transmit
through a current transformer, the information collection device must be associated with the LV setting that is
equivalent to the DMR meter. Relevant information will synchronize with the distributed system meter management
framework such as working hours of information gathering equipment within hour’s afternoon. The connection
between the information concentrator and the meter management framework is terminated using a conventional 2G
or 3G portable system from the measurement management framework, quality control and utilization of information
circulated to the desired client application framework. The 24-hour synchronization cycle means that opportunity
information that can be accessed by the system operator must reflect from the previous time. However, several
opportunities can distinguish as opportunities with high needs. If there is such an opportunity, the DMR meter will
send a quick sign to the concentrator, restore it instantly and communicate information to the meter management
framework. Under the situation of the Caruana’s conductor and deficiencies in the performing arts named
opportunities with high needs power Line Connection in general that can consider as particular strategies where
moods have changed, and after a while, it cannot configure the clutch interface between the central unit and the
instrument. As a result, DMR meters are ready to store various packets in internal memory too.
6 Amam Hossain et al./ Procedia Computer Science 00 (2019) 000–000
Fig.1.Digital metering infrastructure
The length of the package that can store in the memory meter changes from different weeks to different months
depending on the purpose of the estimate and the estimated number of channels used. In this way, it can imagine that
the delay in high requirements for the warning framework will extend. In this case, the adequacy of the estimated
DMR meter data in the first grid operational management, which reaction time must be fast in principle, can be
compromised. Even though the situation is high, that can physically investigate the estimated data from each DMR
meter. An application can send from the measurement management framework or various frameworks set for it. The
useful association results to the DMR meter send the current estimated data to the framework. In both cases, because
of the vulnerability of tasks that meet power line connection requirements, the relationship with the condition of the
meter can be framed, and the information concentrator can stop the request. In this situation, that cannot obtain
current continuous data from the instrument, and the last 4 hours estimated package that can be accessed by the
concentrator collected.DMR meters offer broad prospects for LV power quality opportunities and reflect a 24-hour
delay. Because the entrance to the meter foundation is high, it is possible to form detailed images of past events in
the grid effectively. Also, opportunities in the sample zone, in particular, can be checked by the time of the
inspection, using manual survey usability from the meter management framework. For example, DMS programming
used through Caruana is built into the meter management framework and makes it possible to survey opportunity
data on the quality intensity of the desired area. Also, the current opportunity level data is sufficient to achieve a
general view of power quality issues, presenting tighter opportunities requiring the handling and separation of
information obtained within the framework of meter management. The Generation of DMR meters is currently best
used for reflecting different power quality problems, thus identifying LV systems with, for example, apparent and
intermittent power quality problems. Due to the lack of continuing information, DMR meter inventory is difficult to
use with primary control or voltage control applications in progress and operation.
Fig.2. System architecture Diagram
1154 Amam Hossain Bagdadee et al. / Procedia Computer Science 167 (2020) 1148–1160
Amam Hossain et al / Procedia Computer Science 00 (2019) 000–000 7
E. GSM module
SIM 800 GSM/GPRS is the USB SIM 800 modem highlights the industry standard interface and provides execution
of GSM 900/1800 MHz for SMS, information, and voice with low image frequency and low power consumption.
F. Ultrasonic sensor
Ultrasonic sensors have transmitters and receivers. This distinguishes the separation from the problem by sending
ultrasound after 200 μs, then recognizing the reflected wave. The time is taken from the transmission to the wave
and used by the wave until the recipient accepts it is a way to decide the separation of the object.
5. Functional Description
A. Consumer module
When the customer-side module has turned on, all peripherals installed. After Arduino confirmed the power quality,
a re-check carried out if Arduino paid the customer with a month-end invoice, Arduino operated the power supply
and began recording power unit, after 30 days the information was stored in the data center.
Fig.3. A functional diagram of a consumer module
B. Grid module
The grid-side module confirms the presence of power in the network and transmits the information on inventory
controls in the office focusing on a consistent schedule. This module contains one ultrasonic sensor to detect power
quality, interact with Arduino, communicate about processing activities such as calculating the power supply
capacity in the grid and then using unit information send it to the head office by the GSM.
8 Amam Hossain et al./ Procedia Computer Science 00 (2019) 000–000
Fig.4. A functional diagram of the grid module
6. Proposed System
The general system design has shown in Fig 5. This framework has planned for 2 generation plants of consumer,
and customer of distributed generation. A wireless sensor system to obtain information from these units, the overall
framework has been demonstrated. For solar-based power plants, the power conditioning unit (PCU) promotes
autonomous mode by providing power to input information from the grid for synchronization. Isolation mode for
generation consumer is popular with photovoltaic devices because converters use critical data to send the power
generated to the grid. In the lack of critical data from the detection module, it shows distress or shutdown of the grid.
In such cases, the power supply is turned off in the generating unit, and the connection with the network terminated
until the input collected. In island mode only, it is not an extreme point but can harmonize the respect of the power
produced such as voltage or frequency. The consumer use in the same direction, the sensor only handles
communication; the controller is not like that harmonics. The power quality affected by the customer loads and sent
to the communication controller because there are reasonable concerns to extend the transmission that is a reliable
power supply by maintaining power quality. The association ends with the maintaining the power quality of the
global grid when there are too many assessment levels dropped. The wind power has a controller for synchronizing
the framework at the converter level, and the communication devices collect that information. Wireless sensor nodes
with voltage sensors and power sensors integrated with the transceiver module arrows referred to as the mechanical
sensors shown in Figure 5. These nodes are equipped to form a system through the associated collection nodes
without the need for other communication such as stations base. The adjacent node will act as like as relay and
multi-bounce communication in the system.
Amam Hossain Bagdadee et al. / Procedia Computer Science 167 (2020) 1148–1160 1155
Amam Hossain et al / Procedia Computer Science 00 (2019) 000–000 7
E. GSM module
SIM 800 GSM/GPRS is the USB SIM 800 modem highlights the industry standard interface and provides execution
of GSM 900/1800 MHz for SMS, information, and voice with low image frequency and low power consumption.
F. Ultrasonic sensor
Ultrasonic sensors have transmitters and receivers. This distinguishes the separation from the problem by sending
ultrasound after 200 μs, then recognizing the reflected wave. The time is taken from the transmission to the wave
and used by the wave until the recipient accepts it is a way to decide the separation of the object.
5. Functional Description
A. Consumer module
When the customer-side module has turned on, all peripherals installed. After Arduino confirmed the power quality,
a re-check carried out if Arduino paid the customer with a month-end invoice, Arduino operated the power supply
and began recording power unit, after 30 days the information was stored in the data center.
Fig.3. A functional diagram of a consumer module
B. Grid module
The grid-side module confirms the presence of power in the network and transmits the information on inventory
controls in the office focusing on a consistent schedule. This module contains one ultrasonic sensor to detect power
quality, interact with Arduino, communicate about processing activities such as calculating the power supply
capacity in the grid and then using unit information send it to the head office by the GSM.
8 Amam Hossain et al./ Procedia Computer Science 00 (2019) 000–000
Fig.4. A functional diagram of the grid module
6. Proposed System
The general system design has shown in Fig 5. This framework has planned for 2 generation plants of consumer,
and customer of distributed generation. A wireless sensor system to obtain information from these units, the overall
framework has been demonstrated. For solar-based power plants, the power conditioning unit (PCU) promotes
autonomous mode by providing power to input information from the grid for synchronization. Isolation mode for
generation consumer is popular with photovoltaic devices because converters use critical data to send the power
generated to the grid. In the lack of critical data from the detection module, it shows distress or shutdown of the grid.
In such cases, the power supply is turned off in the generating unit, and the connection with the network terminated
until the input collected. In island mode only, it is not an extreme point but can harmonize the respect of the power
produced such as voltage or frequency. The consumer use in the same direction, the sensor only handles
communication; the controller is not like that harmonics. The power quality affected by the customer loads and sent
to the communication controller because there are reasonable concerns to extend the transmission that is a reliable
power supply by maintaining power quality. The association ends with the maintaining the power quality of the
global grid when there are too many assessment levels dropped. The wind power has a controller for synchronizing
the framework at the converter level, and the communication devices collect that information. Wireless sensor nodes
with voltage sensors and power sensors integrated with the transceiver module arrows referred to as the mechanical
sensors shown in Figure 5. These nodes are equipped to form a system through the associated collection nodes
without the need for other communication such as stations base. The adjacent node will act as like as relay and
multi-bounce communication in the system.
1156 Amam Hossain Bagdadee et al. / Procedia Computer Science 167 (2020) 1148–1160
Amam Hossain et al / Procedia Computer Science 00 (2019) 000–000 9
Fig.5. Monitoring and controlling system in a smart grid
A. System Operation
The parameters monitored by the atmosphere information from in the generation station when using voltage, control
noise, power quality, consumer side, and infinite resources for substation monitoring. The implementation
measurements that must consider for approval are the delay and timing of the power quality analysis. The exchange
rate of data depends on each framework, depending on the frequency of standard and operates carried out by the
system framework. The IoT based WSN operation is selected in the 3.45GHz ISM band, and the equivalent bit is ‘5’
µs. The system of processing of the framework depends on the real-time data.
*
dataPr data load pr
T TT T (13)
Tdata and TPr is the time needed to handle the data specified in equation (13) and corresponds to the amount of data
payload time, and data preparation time of the trailer bit. For example, describing the subtle elements and explaining
the tendency to the field. Power consumed for similar applications can pursue:
tot tx arx pr L
E EE E E (14)
(Etot) Represents the total power considered in communication and is equivalent to all the transmitted power (Etx),
apparent power (Earx), and processing capacity (Epr), and WSN processing substations and there are three stages:
detection, transmission, and preparation. Unobstructed views are considered a method in which two demonstrations
of beam ground reflection and power unbalance calculated. The transmitted power is transmitted through the
receiver to all the processing power and transmission power and transmitted to condition (15), and the power inside
the prop is exponential back off, channel detection, packet transmission, buffering, ability to build inactive
frameworks and devices from rest. The apparent Power gained is a condition (16).
(
)
E E EE
pr
tx
backof f ch sense pkt tx buff
EE
sys idle
EE
wakeup
(15)
22
4
tx rx tx tx rx
R
EGGhh
E
dL
(16)
10 Amam Hossain et al./ Procedia Computer Science 00 (2019) 000–000
B. Simulation Parameters
The system has intended in smart grid for simulation structured with 15kW solar power plants, 15kW wind power
plants, 35kW variable loads, 4kW distributed generation, 5kW load. The IoT Based WSN framework has been
operated with seven nodes for secure communication at the equipment level and tested at the simulation level to
verify multi-hop communication delays and to determine the level of package delivery to make sure the 35 Nodes
system performance has made.
C. Simulation Scenario
This system based on specific scenarios such as grid spread out, demand surpasses generation, satisfying the demand
for production, reduced demand, production is overgrowing, and power quality affects the grid. In this case, an
algorithm for solving such cases has improved and below the algorithm follows:
Fig. 6. Algorithm in Arduino
Amam Hossain Bagdadee et al. / Procedia Computer Science 167 (2020) 1148–1160 1157
Amam Hossain et al / Procedia Computer Science 00 (2019) 000–000 9
Fig.5. Monitoring and controlling system in a smart grid
A. System Operation
The parameters monitored by the atmosphere information from in the generation station when using voltage, control
noise, power quality, consumer side, and infinite resources for substation monitoring. The implementation
measurements that must consider for approval are the delay and timing of the power quality analysis. The exchange
rate of data depends on each framework, depending on the frequency of standard and operates carried out by the
system framework. The IoT based WSN operation is selected in the 3.45GHz ISM band, and the equivalent bit is ‘5’
µs. The system of processing of the framework depends on the real-time data.
*
dataPr data load pr
T TT T (13)
Tdata and TPr is the time needed to handle the data specified in equation (13) and corresponds to the amount of data
payload time, and data preparation time of the trailer bit. For example, describing the subtle elements and explaining
the tendency to the field. Power consumed for similar applications can pursue:
tot tx arx pr L
E EE E E (14)
(Etot) Represents the total power considered in communication and is equivalent to all the transmitted power (Etx),
apparent power (Earx), and processing capacity (Epr), and WSN processing substations and there are three stages:
detection, transmission, and preparation. Unobstructed views are considered a method in which two demonstrations
of beam ground reflection and power unbalance calculated. The transmitted power is transmitted through the
receiver to all the processing power and transmission power and transmitted to condition (15), and the power inside
the prop is exponential back off, channel detection, packet transmission, buffering, ability to build inactive
frameworks and devices from rest. The apparent Power gained is a condition (16).
(
)
E E EE
pr
tx
backof f ch sense pkt tx buff
EE
sys idle
EE
wakeup
(15)
22
4
tx rx tx tx rx
R
EGGhh
E
dL
(16)
10 Amam Hossain et al./ Procedia Computer Science 00 (2019) 000–000
B. Simulation Parameters
The system has intended in smart grid for simulation structured with 15kW solar power plants, 15kW wind power
plants, 35kW variable loads, 4kW distributed generation, 5kW load. The IoT Based WSN framework has been
operated with seven nodes for secure communication at the equipment level and tested at the simulation level to
verify multi-hop communication delays and to determine the level of package delivery to make sure the 35 Nodes
system performance has made.
C. Simulation Scenario
This system based on specific scenarios such as grid spread out, demand surpasses generation, satisfying the demand
for production, reduced demand, production is overgrowing, and power quality affects the grid. In this case, an
algorithm for solving such cases has improved and below the algorithm follows:
Fig. 6. Algorithm in Arduino
1158 Amam Hossain Bagdadee et al. / Procedia Computer Science 167 (2020) 1148–1160
Amam Hossain et al / Procedia Computer Science 00 (2019) 000–000 11
7. Result
Various situations have implemented in the above algorithm, and a simulation assessment has approved the
equivalent. The controller module performs the functions mentioned above, such as dynamic power control
techniques to maintain voltage breakdown. The primary condition of the controller is that the general application of
the network, demand with grid power and power made of the threshold voltage; the framework is in a safe and
functioning working zone. When this condition leveled, if the grid power is lower than consumer demand and
consumer demand is satisfied with the capacity of generation or shortage, the network is operated to be at peak time,
and the tax has not calculated in the same way. In other ways, this case is supposed to be non-stop time, the
consumer demand is lower than the actual generation, and the grid works safely at the power level stored in the
limit. In the following cases, profits decline and the generation of frameworks occurrences a negative impact from
the voltage sage. This demonstrates an increase in voltage, ends at the final target, maintains the power factor, and
causes frequency, causing grid instability and associated loads. After all, things considered, the controller will stop
the connection with distributed generation to the grid, to reduce the grid power. The methodology for controlling
dynamic increases in power flow is called a voltage control procedure that functions and is performed by the
controller. The next part is the appropriate phase, considering the land acquisition model with two beams, which
places the delay in transport the package and the transport rate of the communication in the plotted figure:
Fig.7. The ratio with Number of Packets and number of Nudes
Figure 7 shows the number of packets supplied by the WSN from the plotted graph. It is clear that there are 91
collections of 35 nodes in the system. The measurement of the system changed from 15 to 35, and the quantity of
the package is changed individually from 29 to 91. The investigation has given shows that the proposed strategy is
set more than the current framework as a result of the insensitive thought patterns of transmitted and trouble caused
by EMI / EMC.
Fig.8. The ratio of number of delivery packets and the number of packets
12 Amam Hossain et al./ Procedia Computer Science 00 (2019) 000–000
Figure 8 maps the packet delivery rate versus the number of nodes shown from the Figure 7 above and the range of
94 packages for 80 packages of 34 packages is 84.2. The profile of energy consumption from various system
estimates shows in Figure 9 is a plot of the number of nodes and energy consumption (J).
Fig.9. The ratio of energy consumption and the number of nodes
The primary energy is expected to be 100 J, and the typical energy consumption rate per node is 7.75 J. The
remaining 80J of energy can access after the following transmission of 35 nodes in the system. Along these lines,
the levels of transport and package delay consider, and exemplary implementation attracts attention in the system
with energy consumption of at least 20J.
8. Conclusion
In this paper, implement and approve an effective power control procedures that build a wireless network module
and a control systems stable for controlling separate intelligent network substations from voltage increases and
voltage control technology in parallel monitoring power quality. This system has achieved better reliability in
different situations are distinguished for control methodology requirements such as dynamic power control, single
power quality control. Through this research and planning, the framework provides smart electricity meters with an
environmentally friendly energy productivity framework. As a digital and automated intelligent power meter has
maintained power quality and reduce power quality problem in the network. Theft of power can overcome because
no mechanical part can focus on change. Smart measurement framework based on the power flow sensor neutralized
the weaknesses of the conventional power measurement framework and used for electrical loads. In this paper, those
demonstrate the effective implementation of internet-based methods to monitor electricity supply and usage
situations on a real-time source.
Acknowledgements
This work was supported by Hohai University under China Scholarship Council (CSC) no:2017GXZ019296
References
[1] Jasmeet Chhabra and Punit Gupta,( 2016) “IoT based Smart Home Design using Power and Security Management,” IEEE International
Conference on Innovation and Challenges in Cyber Security, Vol.1, pp. 6-10.
[2] Vamsikrishna Patchava, Hari Babu Kandala, and P. Ravi Babu, (2015)“A Smart Home Automation Technique with Raspberry Pi using
IoT,” IEEE International Conference on Smart Sensors and Systems(IC-SSS).
[3] M. A. Altahrawi, M. Ismail, H. Mahdi, and N. Ramli, (2017) “Routing protocol in a hybrid sensor and vehicular network for different
mobility scenario,” 2017 IEEE 13th Malaysia International Conference on Communications (MICC), Johor Bahru, , pp. 113-118.
[4] R. Janapati, C. Balaswamy, and K. Soundararajan, (2018) “Enhancement of localized routing using CDPSO in WSN,” Conference on
Signal Processing and Communication Engineering Systems (SPACES), Vijayawada, 2018, pp. 16-19.
[5] Padma Nyoman Crisnapati, Nyoman Kusuma Wardana,(2016) “Rudas: Energy and Sensor Devices Management System in Home
Automation,” IEEE publication Symposium (TENSYMP), p.p. 184-187.
Amam Hossain Bagdadee et al. / Procedia Computer Science 167 (2020) 1148–1160 1159
Amam Hossain et al / Procedia Computer Science 00 (2019) 000–000 11
7. Result
Various situations have implemented in the above algorithm, and a simulation assessment has approved the
equivalent. The controller module performs the functions mentioned above, such as dynamic power control
techniques to maintain voltage breakdown. The primary condition of the controller is that the general application of
the network, demand with grid power and power made of the threshold voltage; the framework is in a safe and
functioning working zone. When this condition leveled, if the grid power is lower than consumer demand and
consumer demand is satisfied with the capacity of generation or shortage, the network is operated to be at peak time,
and the tax has not calculated in the same way. In other ways, this case is supposed to be non-stop time, the
consumer demand is lower than the actual generation, and the grid works safely at the power level stored in the
limit. In the following cases, profits decline and the generation of frameworks occurrences a negative impact from
the voltage sage. This demonstrates an increase in voltage, ends at the final target, maintains the power factor, and
causes frequency, causing grid instability and associated loads. After all, things considered, the controller will stop
the connection with distributed generation to the grid, to reduce the grid power. The methodology for controlling
dynamic increases in power flow is called a voltage control procedure that functions and is performed by the
controller. The next part is the appropriate phase, considering the land acquisition model with two beams, which
places the delay in transport the package and the transport rate of the communication in the plotted figure:
Fig.7. The ratio with Number of Packets and number of Nudes
Figure 7 shows the number of packets supplied by the WSN from the plotted graph. It is clear that there are 91
collections of 35 nodes in the system. The measurement of the system changed from 15 to 35, and the quantity of
the package is changed individually from 29 to 91. The investigation has given shows that the proposed strategy is
set more than the current framework as a result of the insensitive thought patterns of transmitted and trouble caused
by EMI / EMC.
Fig.8. The ratio of number of delivery packets and the number of packets
12 Amam Hossain et al./ Procedia Computer Science 00 (2019) 000–000
Figure 8 maps the packet delivery rate versus the number of nodes shown from the Figure 7 above and the range of
94 packages for 80 packages of 34 packages is 84.2. The profile of energy consumption from various system
estimates shows in Figure 9 is a plot of the number of nodes and energy consumption (J).
Fig.9. The ratio of energy consumption and the number of nodes
The primary energy is expected to be 100 J, and the typical energy consumption rate per node is 7.75 J. The
remaining 80J of energy can access after the following transmission of 35 nodes in the system. Along these lines,
the levels of transport and package delay consider, and exemplary implementation attracts attention in the system
with energy consumption of at least 20J.
8. Conclusion
In this paper, implement and approve an effective power control procedures that build a wireless network module
and a control systems stable for controlling separate intelligent network substations from voltage increases and
voltage control technology in parallel monitoring power quality. This system has achieved better reliability in
different situations are distinguished for control methodology requirements such as dynamic power control, single
power quality control. Through this research and planning, the framework provides smart electricity meters with an
environmentally friendly energy productivity framework. As a digital and automated intelligent power meter has
maintained power quality and reduce power quality problem in the network. Theft of power can overcome because
no mechanical part can focus on change. Smart measurement framework based on the power flow sensor neutralized
the weaknesses of the conventional power measurement framework and used for electrical loads. In this paper, those
demonstrate the effective implementation of internet-based methods to monitor electricity supply and usage
situations on a real-time source.
Acknowledgements
This work was supported by Hohai University under China Scholarship Council (CSC) no:2017GXZ019296
References
[1] Jasmeet Chhabra and Punit Gupta,( 2016) “IoT based Smart Home Design using Power and Security Management,” IEEE International
Conference on Innovation and Challenges in Cyber Security, Vol.1, pp. 6-10.
[2] Vamsikrishna Patchava, Hari Babu Kandala, and P. Ravi Babu, (2015)“A Smart Home Automation Technique with Raspberry Pi using
IoT,” IEEE International Conference on Smart Sensors and Systems(IC-SSS).
[3] M. A. Altahrawi, M. Ismail, H. Mahdi, and N. Ramli, (2017) “Routing protocol in a hybrid sensor and vehicular network for different
mobility scenario,” 2017 IEEE 13th Malaysia International Conference on Communications (MICC), Johor Bahru, , pp. 113-118.
[4] R. Janapati, C. Balaswamy, and K. Soundararajan, (2018) “Enhancement of localized routing using CDPSO in WSN,” Conference on
Signal Processing and Communication Engineering Systems (SPACES), Vijayawada, 2018, pp. 16-19.
[5] Padma Nyoman Crisnapati, Nyoman Kusuma Wardana,(2016) “Rudas: Energy and Sensor Devices Management System in Home
Automation,” IEEE publication Symposium (TENSYMP), p.p. 184-187.
1160 Amam Hossain Bagdadee et al. / Procedia Computer Science 167 (2020) 1148–1160
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