Optimal Capacitor Placement to Improve the Performance of the Electrical Power Distribution System Using Genetic Algorithm

Subject Areas : International Journal of Smart Electrical Engineering

Mohammad Hossein Kafi ¹ , Mehdi Mahdavian ² , Ali Asghar Amini ³ , Ghazanfar Shahgholian ⁴ , Majid Dehghani ⁵

1 - Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
2 - Department of Electrical Engineering, Naein Branch, Islamic Azad University, Naein, Isfahan, Iran
3 - Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
4 - Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
5 - Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

Received: 2021-11-06 Accepted : 2021-11-21 Published : 2022-09-01

Keywords:

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3                                International Journal of Smart Electrical Engineering, Vol.10, No.1, Winter 2021                      ISSN: 2251-9246        

EISSN: 2345-6221

 

pp. 1:6

 

Optimal Capacitor Placement to Improve the Performance of the Electrical Power Distribution System Using Genetic Algorithm

Mohammad Hossein Kafi1,3, Mehdi Mahdavian*2, Ali Asghar Amini1,3, Ghazanfar Shahgholian1,3, Majid Dehghani1,3

1 Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

2Department of Electrical Engineering, Naein Branch, Islamic Azad University, Naein, Isfahan, Iran, [e-mail]

3Smart Microgrid Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran

Abstract

The purpose using capacitors in distribution networks is to reduce the total losses of the network. Capacitors help regulate the power factor and voltage in the electrical distribution system, and can be controlled remotely, in and out of the system. Capacitor placement depends on the objective function, which is usually single-objective or multi-objective. In this paper, the amount of capacitor at minimum load is determined using a genetic algorithm. The calculation is done at peak load to determine the sensitivity of power losses. By using this method, the increase of the voltage caused by the lead phase of the system is prevented in the minimum load. A multi-purpose objective function to simultaneously reduce losses and improve the voltage profile of the optimal capacitor size in each section is detected by a genetic algorithm. To show the efficiency of the method, the capacitor placement results are compared using DIGSIENT software.

Keywords: Capacitor Placement, Genetic Algorithm, Performance Improvement, Power Sensitivity Coefficient.

Article history: Received 20-Apr-2021; Revised 01-May-2021; Accepted 15-May-2021.

© 2021 IAUCTB-IJSEE Science. All rights reserved

 

1.       Introduction

 

The development of transmission systems is important in power grids due to the increasing dema­nd load [1-9]. The distribution system is the inte­rface betwe­en the bulk power system and the consumers. The distribution system is part of an electrical power system after the transmission system. Delivery of electrical energy to consumer places is its task [10-13]. Radial distribution system has many applications due to its low cost and simple design [14,15]. When moving away from the substation, the voltage of the buses decreases proportionally and the losses increase secondarily due to the lack of reactive power requi­red [16,17]. Therefore, capacitors are used, and placing the capacitor in the right size is an important issue [18-20]. So far, various studies have been perfo­rmed to determine the optimal location of the capac­itor, which aims to reduce power losses and improve the system voltage profile [21-28].

The problem of simultaneous selection of the optimal location and size of shunt capacitors in three-phase unbalanced radial distribution systems is investigated in [29], which, the main objective function formulated by the two terms energy loss cost, and the cost of capacitor purchase and capa­citor installation, is formed. Has been. Finally, the simulation results of the 25-bus unbalanced radial distribution systems and the 37-bus IEEE are shown.

A two-step method, including loss sensitivity analysis using two sensitivity indicators, and minim­ization of the objective function using the ant colony optimization algorithm, is proposed to identify the optimal locations and capacitance sizes in radial distribution systems in [30].

An analytical method for locating the shunt capacitor optimally in power systems in [31] is presented to achieve the most stable conditions as well as to minimize network losses. Where the modal analysis technique is modified and a new formula is created to calculate the reactive contribution Index of each freight bus based on the inverted reduced Jacobin matrix.

In [32] the ant colony optimization met heuristic is used for minimize the total active losses in electrical distribution systems by means of optimal capacitor bank placement.

A method for solving the capacitor placement problem to determine the minimum required invest­ment is proposed to satisfy the appropriate response constraints in [33], in which a combined method of two algorithms is considered.

A genetic algorithm for optimum shunt capacitor placement in micro-grids in dist­rib­ut­i­o­n networks with consideration of islanded mode of operation is proposed in [34], which the cost function of the proposed opti­mization technique consists of three terms the cost of pow­e­r and energy losses, the cost of capital investments of the ins­talled shunt capacitors and the customers cost of interruption.

A computationally efficient methodology for the optimal location and sizing of static and switched shunt capacitors in radial distribution systems proposed in [35], which this method selects the nodes to be compensated, as well as the optimal capacitor ratings and their operational characteri­stics.

To minimize total power losses and capacitor installation costs, the optimal capacitor placement problem in a radial distribution system using the flower pollination algorithm in [36] is proposed, in which the power flow and network losses with The use of load flow analysis is obtained in data struc­tures.

The water cycle algorithm for placing and optimizing the size of distributed generation units and capacitor banks in [37] is presented to increase system performance, which has different objectives such as minimum power loss, voltage deviation and total energy cost.

To solve the problem of placement and size of shunt capacitors, it proposes optimal radial distr­ibu­tion systems in [38] based on loss sensitivity factors correction. Then, the multiverse optimizer is used to simultaneously search for the most optimal bus passes and the corresponding ca­pa­citor sizes, and then to evaluate the effective­ness of the developed approach, the simulation results on 10, 33 and 69 bus radial distribution syst­ems are shown.

The purpose of this paper is to improve the voltage profile and active power losses by placing a capacitor in the distribution system. The amount of capacitor is measured using loss sensitivity analysis and genetic algorithm. The power dissipation sensit­ivity coefficient for each section provides an impor­tant information feed, which is determined using a load flow study. In a feeder, the parts with the highest power dissipation sensitivity are selected to install the capacitor. A multi-purpose objective fu­nc­tion to simultaneously reduce losses and impr­ove the voltage profile of the optimal capacitor size in each section is detected by a genetic algorithm. DIGSILENT software has been used to show the simulation results of capacitor installation effect.

2.       The system under study

The system studied in this paper is the radial feeder shown in Figure 1. This feeder is very long and the voltage drop and power drop is high. The physical and electrical characteristics of the feeder are presented in Tables 1 and 2 [39].

 

 

Fig. 1.       Geographical diagram of Nasher feeder

2.       Research method and simulation results

By changing the load or system specifications, the amount of losses can be changed, and at the same time the voltage profile can be improved.

 

 

Physical specifications of Nasher feeder

Name	Nashar
Length (m)	87050
Length of right way (m)	46135
Number of section	186
Number of indoor transformer	0
Number of outdoor transformer	87

 

Electrical specifications of Nasher feeder

Parameters	Values
Voltage	20 KV
Active power	4184 KW
Minimum voltage	15.512 KV
Active power losses	577 KW
Current	149.22 A
Power factor	0.809
Percentage of voltage droop	22.44%
Percentage of active power	13.79%

 

Optimal capacitor placement in distribution systems has a number of advantages such as redu­cing losses, improving voltage profile, impro­ving power factor and so on.

A.       Genetic Algorithm:

A genetic algorithm is an algorithm that mimics the natural selection process, and is used to solve finite optimization problems [40,41]. The main differe­nces between the genetic algorithm and the classical optimization methods are the follow­ing:

1. GA works on the encoded strings of the problem parameters. The real quantities of the parameters are obtained from the decoding of these strings.

2. GA searches for different response spaces simultaneously, and reduces the likelihood of getti­ng caught in locally optimized points.

3. In a genetic algorithm, only one response function, called the fit function, needs to be comp­uted. This function expresses the degree of proximity of the response to the objective function of the algorithm.

B.       Capacitor Placement Method:

The steps of capacitor placement method using genetic algorithm are:

(1) Formation of the initial population based on candidate buses.

(2) Evaluate each chromosome in four steps, which are:

(a) Installing the capacitors identified by each of the chromosomes on the candidate buses,

(b) Performing reciprocal load distribution operations,

(c) Calculate the rate of power drop and voltage profile,

and (d) determine a certain value as the degree of competence of the chromosome to it.

(3) Build new populations based on transfer, crossover and mutation operations.

(4) Repeat steps 2 and 3 until the stop parame­ter is reached, which can be the maximum number of repetitions.

(5) Determine the amount of capacitor required by decoding each of the chromosomes.

The capacitor placement algorithm is show in Fig. 1.

C.       Simulation results:

First, the capacitors are placed in the distribution feeder. Then the operation of placing the capacitor on this feeder is done by DIGSILENT software. After placing the capacitor with the genetic algorithm in the feeder of the publisher, and performing load distribution operations with recipr­ocal approach, and calculating the power drop rate and voltage profile, the critical points are identified according to the losses and voltage droop.

The critical part of this feeder are A, B and C. The capacitor values determined for each location for the purpose of the genetic algorithm, and the amount of power loss in each critical section before and after capacitor placement, are shown in Figure 2. The results are also shown in Table 3. The capacitor in each bank is 125 KVAR.

By simulation of Nasher feeder using DIGSILENT software, specifying capacitors place­ment of 130, 250, 480, 1000, and 30 KVAR, this software specifies the places as a, b, c, d, and e. The capacitor for each of place is presented in Table 4.

By exercising the results of the algorithm and the software as well as installing the specified capacitors in the specified and critical sections by the software, the feeder parameters are changed according to Figures. 3. The results are also shown in Table 5.

 

 

Fig. 2.       Capacitor placement algorithm

 

 

 

Fig. 3.       The capacitor size required in each section

 

 

Fig. 4.       The specification of the feeder before and after capacitor placing

 

Capacitor size required in each section

Section name	Capability of installed capacitor (KVAR)	Power losses before capacitor placement In each section (KW)	Power losses after capacitor placement In each section (KW)
A	125	27.78	18.25
B	250	69.89	46.49
C	750	49.3	33.95

 

The specification of the feeder before and after capacitor placing

Section name	a	b	c	d	e
Capability of capacitor (KVAR)	1000	480	250	130	30

 

The specification of the feeder before and after capacitor placing

specification	Before capacitor placement	after capacitor placement by intelligent method	after e capacitor placement by DIGSILENT software
Current in head of feeder (A)	149.27	119.64	116.31
Voltage (kV)	15.509	17.3	17.87
Power factor	0.809	0.991	0.982
Power losses in feeder (kW)	577	391	347

 

In figs. 5, 6 and 7, the voltage profile before and after capacitor placement using two methods are depicted.

 

 

Fig. 5.       The Nasher feeder voltage profile before placing the capacitor

 

Fig. 6.       Nasher voltage feeder profile after placing the capacitor by intelligent method

 

Fig. 7.       Nasher feeder voltage profile after placing the capacitor by DIGSILENT software

1.       Conclusions

The use of capacitors in distribution networks, and their optimal location in the distribution system, is an important and fundamental necessity. A multifunctional objective function, for simultaneous improvement of several parameters, in determining the capacitance size, is proposed in this paper, which is solved using a genetic algorithm.

The installation location of the fixed capacitor was obtained by the fast load distribution method, which is a reciprocal method. The algorithm is implemented on a very long radial feeder, which results in a significant reduction in losses, and a relative improvement in voltage and power quality profiles. With the reduct­ion of losses, part of the line capacity was freed up, and finally the costs of developing new resources were reduced. Also, with the relative improvement in the quality of the voltage profile, the quality of the power supply also increased.

By comparing the results of placing the capacitors intelligently using DIGSILENT software, it was shown that the presentation method has more economic advantages than the software method, because the software while creating more improve­ement in losses and voltage profiles, but more capacitors are used that are not cost effective.

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