Next Article in Journal
Prescribed Performance Adaptive Backstepping Control for Winding Segmented Permanent Magnet Linear Synchronous Motor
Next Article in Special Issue
Nonlinear Analysis for a Type-1 Diabetes Model with Focus on T-Cells and Pancreatic β-Cells Behavior
Previous Article in Journal / Special Issue
Chaos Synchronization for Hyperchaotic Lorenz-Type System via Fuzzy-Based Sliding-Mode Observer
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

A Novel Method of Optimal Capacitor Placement in the Presence of Harmonics for Power Distribution Network Using NSGA-II Multi-Objective Genetic Optimization Algorithm

by
Majid Ebrahimi Moghadam
1,
Hamid Falaghi
1,* and
Mahdi Farhadi
2,*
1
Electric and Computer Engineering Department, University of Birjand, Birjand, Iran
2
Computer and Industries Department, Birjand University of Technology, Birjand, Iran
*
Authors to whom correspondence should be addressed.
Math. Comput. Appl. 2020, 25(1), 17; https://doi.org/10.3390/mca25010017
Submission received: 6 January 2020 / Revised: 16 March 2020 / Accepted: 16 March 2020 / Published: 19 March 2020
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2019)

Abstract

One of the effective ways of reducing power system losses is local compensation of part of the reactive power consumption by deploying shunt capacitor banks. Since the capacitor’s impedance is frequency-dependent and it is possible to generate resonances at harmonic frequencies, it is important to provide an efficient method for the placement of capacitor banks in the presence of nonlinear loads which are the main cause of harmonic generation. This paper proposes a solution for a multi-objective optimization problem to address the optimal placement of capacitor banks in the presence of nonlinear loads, and it establishes a reasonable reconciliation between costs, along with improvement of harmonic distortion and a voltage index. In this paper, while using the harmonic power flow method to calculate the electrical quantities of the grid in terms of harmonic effects, the non-dominated sorting genetic (NSGA)-II multi-objective genetic optimization algorithm was used to obtain a set of solutions named the Pareto front for the problem. To evaluate the effectiveness of the proposed method, the problem was tested for an IEEE 18-bus system. The results were compared with the methods used in eight other studies. The simulation results show the considerable efficiency and superiority of the proposed flexible method over other methods.
Keywords: optimal capacitor placement; harmonic power flow; NSGA-II multi-objective genetic optimization algorithm; Pareto front optimal capacitor placement; harmonic power flow; NSGA-II multi-objective genetic optimization algorithm; Pareto front

Share and Cite

MDPI and ACS Style

Ebrahimi Moghadam, M.; Falaghi, H.; Farhadi, M. A Novel Method of Optimal Capacitor Placement in the Presence of Harmonics for Power Distribution Network Using NSGA-II Multi-Objective Genetic Optimization Algorithm. Math. Comput. Appl. 2020, 25, 17. https://doi.org/10.3390/mca25010017

AMA Style

Ebrahimi Moghadam M, Falaghi H, Farhadi M. A Novel Method of Optimal Capacitor Placement in the Presence of Harmonics for Power Distribution Network Using NSGA-II Multi-Objective Genetic Optimization Algorithm. Mathematical and Computational Applications. 2020; 25(1):17. https://doi.org/10.3390/mca25010017

Chicago/Turabian Style

Ebrahimi Moghadam, Majid, Hamid Falaghi, and Mahdi Farhadi. 2020. "A Novel Method of Optimal Capacitor Placement in the Presence of Harmonics for Power Distribution Network Using NSGA-II Multi-Objective Genetic Optimization Algorithm" Mathematical and Computational Applications 25, no. 1: 17. https://doi.org/10.3390/mca25010017

APA Style

Ebrahimi Moghadam, M., Falaghi, H., & Farhadi, M. (2020). A Novel Method of Optimal Capacitor Placement in the Presence of Harmonics for Power Distribution Network Using NSGA-II Multi-Objective Genetic Optimization Algorithm. Mathematical and Computational Applications, 25(1), 17. https://doi.org/10.3390/mca25010017

Article Metrics

Back to TopTop