Direct Current Control of Grid Connected Two Level Inverter With LCL-Filter Using Deep Reinforcement Learning Algorithm

Abstract

This work presents a novel control paradigm to improve the Direct Current Regulation (DCR) of two-level inverters that are connected to the grid with LCL filters. The Deep Reinforcement Learning (DRL) based Deep Deterministic Policy Gradient (DDPG) algorithm is utilized to address the constraints of traditional control methods, such as Proportional Integral (PI) controllers and Model Predictive Control (MPC). The suggested method tackles challenges like as non-linearity, model dependency, and parameter fluctuations, which have a substantial impact on the performance of the DCR of grid connected two-level inverters of electric power system. The DDPG algorithm offers a flexible control technique that enables adaptive learning and optimization of policies. The results are validated in Real time mode Hardware In Loop (HIL) using Opal-RT & Texas instrument launchpad. Simulations performed on the MATLAB platform provide a reliable testing environment to assess the effectiveness of the proposed controller compared to traditional alternatives. The simulation results clearly show that the DDPG-based controller performs better than any other controller. This strategy surpasses traditional methods, demonstrating an increased resistance to reliance on specific models and uncertainties in parameters.

Keywords

• Deep reinforcement learning
• direct current regulation
• grid-connected inverters
• LCL filters
• deep deterministic policy gradient
• PI controller