Hardware Implementation of Hybrid Data Driven-PI Control Scheme for Resilient Operation of Standalone DC Microgrid

Abstract

The control of energy storage systems (ESSs) within autonomous microgrids (MGs) is critical for ensuring stable and efficient operation, especially when incorporating renewable energy resources (RESs) such as photovoltaic (PV) systems. This paper addresses managing a standalone DC microgrid that combines PV generation and a battery energy storage system (BESS). We propose a hybrid control strategy that combines a Recurrent Neural Network (RNN) with Proportional-Integral (PI) controllers to improve the performance of the bidirectional converter that connects the BESS to the microgrid. The RNN processes the voltage error and derivative into a reference current, which a PI controller refines to determine the best duty cycle for the converter’s switches. This hybrid control scheme provides superior adaptability and performance in various load conditions, including pulsed power load (PPL) demands. Simulation results show that the proposed control method exceeds traditional PI-PI control algorithms, particularly in improving the transient stability of the DC bus voltage and optimizing BESS performance. We conducted extensive hardware experiments to verify the robustness and effectiveness of the developed control algorithm. The experimental results confirmed the superior performance of the hybrid RNN-PI control scheme, demonstrating its ability to maintain system stability and efficiency across a wide range of real-world scenarios. This experimental validation reflects the reliability and effectiveness of the proposed control strategy in improving microgrid operations.

Keywords

• energy storage system
• renewable energy resource
• recurrent neural network
• DC microgrid
• hybrid RNN-PI
• pulsed power load