A NOVEL REINFORCEMENT LEARNING - BASED CONTROL APPROACH FOR GRID-INTEGRATED PV-FESS SYSTEMS USING INDUCTION MOTORS

Lại Thị Thanh Hoa; Nguyễn Thị Mai Hương; Đỗ Trung Hải; Lại Khắc Lãi; Nguyễn Đức Toàn

doi:10.34238/tnu-jst.12260

A NOVEL REINFORCEMENT LEARNING - BASED CONTROL APPROACH FOR GRID-INTEGRATED PV-FESS SYSTEMS USING INDUCTION MOTORS

About this article

Received: 10/03/25 Revised: 09/05/25 Published: 09/05/25

Authors

1. Lai Thi Thanh Hoa , TNU - University of Technology
2. Nguyen Thi Mai Huong, TNU - University of Technology
3. Do Trung Hai, TNU - University of Technology
4. Lai Khac Lai, TNU - University of Technology
5. Nguyen Duc Toan, School of Mechanical Engineering - Hanoi University of Science and Technology

Abstract

This study introduces a novel reinforcement learning-based control strategy for a grid-connected photovoltaic system integrated with a flywheel energy storage system. The proposed method replaces the conventional dual-loop current control of the Field-Oriented Control scheme for the induction motor within the FESS with a single-agent controller based on the Deep Deterministic Policy Gradient algorithm. This intelligent controller leverages the strengths of Reinforcement Learning to handle the nonlinearities and parameter uncertainties inherent in the Flywheel Energy Storage System. Simulations in MATLAB/Simulink evaluate the performance of the proposed control system under various operating conditions. Results demonstrate that the Deep Deterministic Policy Gradient -based controller outperforms traditional Proportional-Integral controllers in ensuring stable power output to the grid, even under significant fluctuations in photovoltaic generation. The proposed control method enhances system stability, optimizes energy storage dynamics, and maintains power quality, contributing to the broader adoption of intelligent energy storage solutions in renewable energy integration.

Keywords

Deep deterministic policy gradient; Flywheel energy storage system; Induction motor; Microgrid; Reinforcement learning

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DOI: https://doi.org/10.34238/tnu-jst.12260

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