Robust Optimization Algorithms for Solving Automatic Generation Control of Multi-Constrained Power System

Abstract

This chapter presents four effective evolutionary methods, namely grey wolf optimization (GWO), symbiotic organism search (SOS), JAYA, and teaching-learning-based optimization (TLBO), for solving automatic generation control (AGC) problem in power system. To show the effectiveness, two widely used interconnected power plants are examined. To extract maximum possible generation, distinct PID-controllers are designed employing ITAE-based fitness function. Further, to enhance the dynamic stability of concerned power systems, 2DOF-PID controllers are proposed in LFC area and optimally designed using aforesaid algorithms. To demonstrate the supremacy, obtained results are compared with some existing control algorithms. Moreover, robustness of the designed controller is believed under the action of random load perturbation (RLP). Finally, sensitivity analysis is carried out to show the stability of the designed system under loading and parametric disturbance conditions.