Hybrid evolutionary algorithm based fuzzy logic controller for automatic generation control of power systems with governor dead band non-linearity

Abstract

A new intelligent Automatic Generation Control (AGC) scheme based on Evolutionary Algorithms (EAs) and Fuzzy Logic concept is developed for a multi-area power system. EAs i.e. Genetic Algorithm–Simulated Annealing (GA–SA) are used to optimize the gains of Fuzzy Logic Algorithm (FLA)-based AGC regulators for interconnected power systems. The multi-area power system model has three different types of plants i.e. reheat, non-reheat and hydro and are interconnected via Extra High Voltage Alternate Current transmission links. The dynamic model of the system is developed considering one of the most important Governor Dead Band (GDB) non-linearity. The designed AGC regulators are implemented in the wake of 1% load perturbation in one of the control areas and the dynamic response plots are obtained for various system states. The investigations carried out in the study reveal that the system dynamic performance with hybrid GA–SA-tuned Fuzzy technique (GASATF)-based AGC controller is appreciably superior as compared to that of integral and FLA-based AGC controllers. It is also observed that the incorporation of GDB non-linearity in the system dynamic model has resulted in degraded system dynamic performance.

Keywords

• automatic generation control
• governor dead band
• non-linearity
• fuzzy logic algorithm
• evolutionary algorithms