Mitigating voltage quality issues in Sawla Substation, Ethiopia: A dynamic voltage restorer controlled by neural networks and fuzzy logic - Balakumar Subramanian; Alazar Demamu; Andinet Anjamo; Muluneh Lemma; Yalisho Girma (Results in Engineering)
Ensuring power quality in distribution systems is critical due to voltage sags, swells, and harmonic distortions, which affect system performance and equipment reliability. The Sawla distribution system in Ethiopia experiences high Total Harmonic Distortion (THD), exceeding IEEE Std 519–2014 limits due to nonlinear loads at 15 KV. This study proposes an Artificial Neural Network (ANN) and Fuzzy Logic Controller (FLC)-based Dynamic Voltage Restorer (DVR) to mitigate voltage fluctuations. Using a Pulse Width Modulation (PWM) inverter and d-q transformation, the DVR dynamically compensates voltage variations. ANN-based control outperforms FLC, achieving THD reduction from 23.92 % to 4.94 %, while the FLC-controlled DVR achieves 12.63 % THD, which remains above IEEE limits. MATLAB/Simulink simulations confirm the DVR’s effectiveness in stabilizing voltage and reducing harmonic distortions. This approach enhances power quality and provides a scalable solution for distribution networks.