Proposed PSO enhanced deep ANN controllers improve voltage regulation and reduce harmonic distortion in power distribution networks, suggesting better compliance with standards.
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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Tyover et al. (2025) studied this question.
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