Quantum Reinforcement Learning–Enabled DSTATCOM for Transient Power Quality Stabilization in High-Penetration PV Distribution System

Document Type : Research Article

Authors
Mohammed Nadeem Iqbal 1
Asini Kumar Baliarsingh 2
Pratap Sekhar Puhan 3
1 Department of Electrical Engineering, Biju Pattnaik University of Technology, Rourkela, Odisha, India
2 Electrical Engineering, Government College of Engineering Kalahandi, Odisha, India
3 Electrical and Electronics Engineering, Sreenidhi institute of science and Technology Hyderabad, India
10.22059/jser.2026.407887.1683
Abstract
This study proposes a Quantum Reinforcement Learning-based Distribution Static Compensator (QRL-DSTATCOM) for transient power quality enhancement in high-penetration photovoltaic (PV) distribution systems. Increasing integration of PV generation introduces significant challenges, including voltage instability, harmonic distortion, and power factor degradation due to stochastic irradiance and rapid load fluctuations. To address these issues, a hybrid quantum-classical reinforcement learning framework is developed, incorporating quantum state encoding and amplitude-enhanced exploration to improve learning efficiency and control performance. The proposed QRL-DSTATCOM enables faster convergence and superior policy optimization compared to conventional RL-based compensators. The system is modeled and validated in MATLAB/Simulink under diverse operating scenarios, including variable irradiance, sudden load changes, and grid fault conditions. Simulation results demonstrate that the proposed approach achieves 43–50% faster stabilization time, significantly reduced total harmonic distortion (THD) well within IEEE-519 limits, and maintains bus voltage within ±5% of nominal values under severe disturbances. Overall, the framework validated using OPAL-RT real time software and presents a scalable and intelligent solution for next-generation smart distribution networks with high renewable energy penetration, ensuring improved stability, robustness, and power quality performance.
Keywords
Photovoltaic
DSTATCOM
Quantum Reinforcement Learning
Power Quality
Reactive Power Compensation
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Journal of Solar Energy Research
Volume 11, Issue 2
Spring 2026
Pages 3890-3908