Meta-heuristic Approaches for Solving Automatic Generation Control Problems: A Brief Review

Any fluctuation in the power demand from its nominal value results in system frequency variations, leading to undesirable effects on the system. In order to overcome such circumstances Automatic Generation Control (AGC) is adopted. Various controllers like Proportional Integral Derivative (PID), Linear-Quadratic Regulator (LQR), Model Predictive Controller (MPC), and intelligent controllers like Fuzzy Logic Controllers and ANN Controllers are employed as secondary control to solve AGC problems. However, the controllers alone are not enough, and therefore requiring further improvement by optimization of the gains and parameters of the controller, for better and efficient operation of the power system. Till now many meta-heuristic optimization algorithms are available for solving automatic generation control problems. This paper addresses a brief outline of significant optimization approaches employed for optimization of power system parameters for load frequency regulation. The various meta-heuristic techniques, namely, Genetic Algorithm (GA), Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Salp Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA) and few hybrid techniques are highlighted in this article.