Application of Advanced Soft Computing Techniques for Harmonic Reduction Using Active Power Filters in Radial Distribution System

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 6

Year of Publication : 2024

Authors : Ashokkumar Lakum, Rakesh Parmar, Rahul Keshwala, Anita Parmar

10.14445/23488379/IJEEE-V11I6P121

How to Cite?

Ashokkumar Lakum, Rakesh Parmar, Rahul Keshwala, Anita Parmar, "Application of Advanced Soft Computing Techniques for Harmonic Reduction Using Active Power Filters in Radial Distribution System," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 6, pp. 188-196, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I6P121

Abstract:

This paper addresses the use of advanced soft computing techniques, namely the Driving Training-Based Optimization algorithm (DTBO) and Coati Optimization Algorithm (COA), to reduce the harmonics through the use of active Power Filters (APFs). Distributed Generation (DG), like Solar Photo Voltaic (SPV) gained much interest due to its advantages. However, there are issues with Power Quality (PQ), including harmonic distortion, when SPVs are integrated into the Radial Distribution System (RDS). The harmonics are injected into the RDS by Nonlinear Loads (NLs). Here, NL at two end nodes is considered in addition to nonlinear DG (NLDG). APFs are used to decrease the harmonics to specified limits. In this instance, APFs are placed correctly to reduce harmonics and improve PQ. Within limitations on inequality, optimization seeks to minimize APF's current. DTBO optimizes the APF's size at the optimal bus location. The DTBO is inspired by natural processes and contains features that are well-balanced for both exploration and exploitation. A simulation is run on the IEEE-69 bus RDS to assess the DTBO's performance. It is compared with the recently published advanced soft computing technique COA. The simulation results confirm the stability and efficacy of the DTBO method in handling this optimization problem.

Keywords:

Advanced soft computing, Driving training-based optimization algorithm, Harmonics, Radial distribution system, Power system optimization.

References:

[1] Utkarsh Pandey et al., “Applications of Artificial Intelligence in Power System Operation, Control and Planning: A Review,” Clean Energy, vol. 7, no. 6, pp. 1199-1218, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Fuchs, Ewald F, and Mohammad AS Masoum, Power Quality in Power Systems and Electrical Machines: Academic Press, 2011.
[Google Scholar] [Publisher Link]
[3] Boopathi, Rajendran, and Vairavasundaram Indragandhi, “Comparative Analysis of Control Techniques Using a PV-Based SAPF Integrated Grid System to Enhance Power Quality,” e-Prime-Advances in Electrical Engineering, Electronics and Energy, vol. 5, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Narinder Kumar, and Ashwani Kumar, “Techno-Economic Analysis of Non-Linear DG Penetration in Radial Distribution Systems,” Distributed Generation & Alternative Energy Journal, vol. 32, pp. 54-74, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[5] George J. Wakileh, Power Systems Harmonics: Fundamentals, Analysis and Filter Design: Springer, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Arrillaga, Jos, and Neville R. Watson, Power System Harmonics Analysis: John Wiley & Sons, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Iman Ziari, and Alireza Jalilian, “Optimal Placement and Sizing of Multiple APLCs using a Modified Discrete PSO,” International Journal of Electrical Power & Energy Systems, vol. 43, no. 1, pp. 630-639, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Langella Roberto, Alfredo Testa, and Et Alii, “IEEE Recommended Practice and Requirements for Harmonic Control in Electric Power Systems,” IEEE, New York, NY, pp.1-29, 2014.
[CrossRef] [Google Scholar] [Publisher Link]  
[9] Hamed Rezapour et al., “Enhancing Power Quality and Loss Optimization in Distorted Distribution Networks Utilizing Capacitors and Active Power Filters: A Simultaneous Approach,” International Journal of Electrical Power & Energy Systems, vol. 155, pp. 1-17, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Iman Ziari, and Alireza Jalilian, “Optimal Allocation and Sizing of Active Power Line Conditioners using a New Particle Swarm Optimization-Based Approach,” Electric Power Components and Systems, vol. 40, pp. 273-291, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Zong Woo Geem, Joong Hoon Kim, and G.V. Loganathan, “A New Heuristic Optimization Algorithm: Harmony Search,” Simulation, vol. 76, no. 2, pp. 60-68, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Masoud Farhoodnea et al., “Optimum Placement of Active Power Conditioner in Distribution Systems Using Improved Discrete Firefly Algorithm for Power Quality Enhancement,” Applied Soft Computing, vol. 23, pp. 249-258, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Swati Gade and Rahul Agrawal, “Optimal Utilization of Unified Power Quality Conditioner Using the JAYA Optimization Algorithm,” Engineering Optimization, vol. 55, no.1,  pp. 1-18, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Mukesh Pushkarna et al., “An Optimal Placement and Sizing of Type-IV DG with Reactive Power Support using UPQC in an Unbalanced Distribution System Using Particle Swarm Optimization,” Energy Systems, vol. 15, pp. 353-370, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Kapse Shilpa S. Shrawane, Manoj B. Daigavane, and Prema M. Daigavane, “Optimal Localization and Sizing of UPFC to Solve the Reactive Power Dispatch problem under Unbalanced Conditions,” IETE Journal of Research, vol. 66, pp. 396-413, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Wolpert David H, and William G. Macready, “No Free Lunch Theorems for Optimization,” IEEE Transactions on Evolutionary Computation, vol. 1, no.1,  pp. 67-82, 1997.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Mohammad Dehghani, Eva Trojovská, and Pavel Trojovský, “A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems on the base of Simulation of Driving Training Process,” Scientific Reports, vol. 12, pp. 1-21, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Anand Muni Mishra et al., “Driving a Key Generation Strategy with Training-based Optimization to Provide safe and Effective Authentication Using Data Sharing Approach in IoT Healthcare,” Computer Communications, vol. 212, pp. 407-419, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Guoqiang Zhang et al., “Driver Training Based Optimized Fractional Order PI-PDF Controller for Frequency Stabilization of Diverse Hybrid Power System,” Fractal and Fractional, vol. 7, no. 4, pp. 1-23, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Mohammad Dehghani et al., “Coati Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems,” Knowledge-Based Systems, vol. 259, pp. 1-43, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Emine Baş, and Gülnur Yildizdan, “Enhanced Coati Optimization Algorithm for Big Data Optimization Problem,” Neural Processing Letters, vol. 55, pp. 10131-10199, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Iman Ziari, and Alireza Jalilian, “A New Approach for Allocation and Sizing of Multiple Active Power-Line Conditioners,” IEEE Transactions on Power Delivery, vol. 25, no. 2, pp. 1026-1035, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Jen-Hao Teng, and Chuo-Yean Chang, “Backward/Forward Sweep-Based Harmonic Analysis Method for Distribution Systems,” IEEE Transactions on Power Delivery, vol. 22, no. 3, pp. 1665-1672, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[24] J. S. Savier, and Debapriya Das, “Impact of Network Reconfiguration on Loss Allocation of Radial Distribution Systems,” IEEE Transactions on Power Delivery, vol. 22, no. 4, pp. 2473-2480, 2007.
[CrossRef] [Google Scholar] [Publisher Link]