Comparative Performance Investigation of Genetic Algorithms (GAs), Particle Swarm Optimization (PSO) and Bacteria Foraging Algorithm (BFA) Based Automatic Generation Control (AGC) with Multi Source Power Plants (MSPPs)

Abstract

This paper presents comparative performance analysis of GAs, PSO and BFA technique-based AGC regulators with MSPPs in each system. Each control area consists of hydro, thermal and gas power plants (HTGPPs) for generation of electricity. In this work, proportional integral derivative (PID) control structure-based AGC regulators are designed by using the above intelligent techniques. However, the cost function for designing of these intelligent controllers is formulated using the integral square error (ISE) performance index. In the previous AGC studies, simulation time for tuning of above intelligent AGC controller to analyze performance was not reported. Therefore, this research work proposes a new study to investigate the performance of these intelligent AGC controllers based upon performance indicators: (i) simulation time of these techniques, (ii) first peak value of transient response, (iii) oscillations in transient response and (iv) settling time of transient response. The parameters of the controllers are evaluated using these techniques and investigations are carried out to find the best performance of the system. Besides, system dynamic performance of BFA-tuned AGC controllers are also demonstrated for +1% to +4% step load disturbance in the respective control areas.