Fuzzy Gain Scheduling Controllers for Automatic Generation Control of Two-area Interconnected Electrical Power Systems

Abstract

In this study, fuzzy gain scheduling controllers are proposed for automatic generation control of interconnected electrical power systems. Primarily, the study is done for automatic generation control of a two-area non-reheat thermal power system, and the parameters of fuzzy gain scheduling controllers are optimized by a genetic algorithm. Simulation results show the performance of fuzzy gain scheduling controllers is superior compared to the optimal and controllers based upon the gravitational search, the bacteria foraging optimization, and the hybrid bacteria foraging optimization–particle swarm optimization algorithms for an identical power system. The proposed approach is further protracted to a two-area reheat thermal system; the benefits of the fuzzy gain scheduling approach are demonstrated over optimal, conventional proportional-integral, and genetic algorithm-based integral controllers. Next, a multi-source multi-area hydro thermal system is considered, and the superiority of fuzzy gain scheduling controllers is established by comparing the results to the genetic algorithm and best claimed hybrid firefly algorithm–pattern search technique-based controllers. Finally, the effectiveness of the proposed approach is established for a two-area restructured reheat thermal power system. The simulation results indicate that the proposed fuzzy gain scheduling controllers work efficiently and provide better dynamic performance without being redesigned for separate systems.