Article Dans Une Revue Asian Journal of Advanced Research and Reports Année : 2025

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

Résumé

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.

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Dates et versions

hal-05203454 , version 1 (08-08-2025)

Identifiants

  • HAL Id : hal-05203454 , version 1

Citer

Ayakpam P Tyover, Evans C Ashigwuike. PSO Enhanced and Deep ANN Control for Voltage Regulation and Harmonic Mitigation in Electrical Distribution Networks. Asian Journal of Advanced Research and Reports, 2025, 19 (8), pp.101-126. ⟨hal-05203454⟩
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