Hybrid Improved Teaching Learning-Based Optimization and Differential Evolution (hITLBO-DE)-Based Optimization of Multi-area Thermal Power System with Automatic Generation Control

摘要

Stability and integrity of the power system being a major concern requires the system parameters to be monitored. The problem considered in the paper is an improvement in functioning of automatic generation control (AGC) in a cross-connected power system with different approaches. The objective of the paper is facilitating an approach to design a fast and precise controller for the power system with unequal areas, and tuning the response of AGC is a multi-objective function with constraints. The initial approach requires verifying the conventional objective function to be implemented for the formation of the proposed approach. This paper deals with exploration of a proportional integral derivative (PID) controller with a hybrid improved teaching learning-based optimization and differential evolution (hITLBO-DE) being studied for controlling system response. The major focus in the hybrid is to improve the learning process and achieve a faster and effective learning. Various cases of load variation are considered for the analysis such as 10% change in area-1 (Case-1), 10% change in area-2 (Case-2), and 10% change in both the areas simultaneously (Case-3). The analysis of results for all the 3 cases confirms the effectiveness of the proposed scheme. Also, a significant improvement in the response time of the system is observed.