Deep Reinforcement Learning Framework for Adaptive Power Quality Management in Hybrid Microgrid
TL;DR
Convergence and multi-run statistical analysis further confirm the robustness, stability, and reproducibility of the trained policy, demonstrating the effectiveness of DRL as an intelligent and scalable solution for next-generation microgrid PQ control.
Abstract
Hybrid microgrids integrating photovoltaic (PV) arrays, wind tur-binedriven PMSG units, fuel cells, and battery storage enhance sustainability but face serious power quality (PQ) challenges due to the intermittent and nonlinear behavior of renewable sources and loads. Traditional PI, PR, and hybrid intelligent controllers offer acceptable nominal performance but lack adaptability and predictive capability under rapidly varying disturbances. To overcome these limitations, this paper proposes a Deep Reinforcement Learning (DRL) frame-work based on the Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm for real-time PQ management, where a multi-objective reward function guides optimal actions for voltage regulation, harmonic suppression, unbalance mitigation, and frequency stability. Vali-dation in a MATLAB/Simulink hybrid microgrid with nonlinear loads and renewable intermittency shows that the proposed DRL controller reduces THD from 8.42% to 2.11%, VUF from 3.9% to 0.7%, and frequency deviation from 0.42 to 0.08 Hz, while improving settling time by nearly 50%. Convergence and multi-run statistical analysis further confirm the robustness, stability, and reproducibility of the trained policy, demonstrating the effectiveness of DRL as an intelligent and scalable solution for next-generation microgrid PQ control.