Deep Reinforcement Learning Technique for Automatic Voltage Regulation

This article focuses on the major components of an Automatic Voltage Regulator (AVR) and aims to improve the performance of AVR by improving its transient response, adaptation to changing conditions, and resilience. Its goal is to use deep reinforcement learning (DRL) to operate the AVR system. The research is significant because it compares the performance of the system without a controller, the PID controller, the FPID, and the suggested DRL-FPID. This paper demonstrates an innovative application of DRL-FPID for the AVR system taking into account its enhanced control characteristics and raised tuning flexibility over the conventional PID regulator. Based on the suggested approach's superior performance of AVR's voltage stability and comparative MATLAB/Simulink results with graphs and tables are presented in this study and can be used to ease real-time operations.