PSO Enhanced and Deep ANN Control for Voltage Regulation and Harmonic Mitigation in Electrical Distribution Networks

• Published in: Asian Journal of Advanced Research and Reports Vol. 19; no. 8; pp. 101 - 126
• Main Authors: Tyover, Ayakpam P., Ashigwuike, Evans C.
• Format: Journal Article
• Language: English
• Published: 07.08.2025
• Subjects: Humanities and Social Sciences
• DOI: 10.9734/ajarr/2025/v19i81118
• ISSN: 2582-3248

Modern electrical distribution networks face escalating power quality challenges, including voltage sags/swells and harmonic distortion exceeding IEEE Std 519-2022 limits, driven by renewable integration and non-linear loads. To address these, this study proposed novel particle swarm-enhanced and deep artificial neural network (ANN) controllers for Dynamic Voltage Restorers (DVRs), featuring competitive Particle Swarm Optimisation (PSO) and a 7-layer deep ANN to optimise voltage regulation and harmonic suppression. Validated in MATLAB/Simulink on Nigeria’s Ibadan Distribution Network (IEEE 33-bus system) under multifault scenarios (three-phase sags, sag-induced faults, and combined disturbances), the framework achieved  voltage stability (restoring voltage to ). It reduced total harmonic distortion (THD) to , outperforming conventional PI controllers (THD >8.5%) and standalone AI methods with 65% faster convergence. The ANN-DVR excelled in complex fault mitigation (THD: 1.78–2.26%), while the PSO-DVR offered computational efficiency (THD: 1.85–2.53%), together providing a robust solution for modern distribution grids requiring stringent power quality compliance.