Modeling and performance analysis of artificial intelligence (AI) based controllers for AVR of a synchronous generator (SG)

Abstract

An automatic voltage regulator (AVR) is an electronic device used to control, adjust, and maintain a constant voltage level at the stator terminals of a synchronous generator (SG). Hence, the voltage stability of a power system network is affected by AVR’s performance. Maintaining constancy and stability of the nominal voltage level in power systems remains a major control problem. Another critical reason for effective control of the generator's terminal voltage is that real line losses are determined by the real and reactive power flows and variation in terminal voltage has a large effect on reactive power flow and thus on these losses. A large power system consists of several synchronous generators that operate in synchronism; the terminal voltage and frequency are to be kept constant with minimal variation to ensure the stability of the power system. The voltage stability of a synchronous generator is highly affected when the terminal voltage varies above the nominal acceptable range. To maintain a constant voltage at a SG’s terminal, an AVR is used. The performance of an AVR is highly dependent on efficient controller design, which improves the output of the AVR by restoring the voltage of the synchronous generator to its nominal value in the presence of disturbances. The selection of a suitable controller is one of the most challenging aspects of AVR system design. This study presents the design, modeling, and performance analysis of an AVR system employing a Proportional Integral and Derivative (PID) controller, a Fuzzy Logic controller (FLC), and a Model Predictive Controller (MPC) for the performance enhancement and transient response of the AVR system with these controllers. Initially, a transfer function is used to develop a mathematical model of an AVR in order to observe its step response when the terminal voltage of a generator is disturbed. A PID controller is then added to the system and tuned to enhance the step response of an AVR. The third model develops and implements an AVR system based on MPC, while the final model implements an FLC for an AVR system. Simulating the models in Matlab Simulink 2021a, the results have demonstrated the need for a controlling mechanism to enhance the dynamic performance of the AVRS, and MPC has shown to be the most effective controller.