Fractional order fuzzy pid controller for automatic generation control of power systems

Abstract

In the continuously growing size and structure of the contemporary power system, when dealing with load demand uncertainties, the use of intelligent Automatic Generation Control (AGC) strategy is very much necessary for satisfactory operation of the power system. In this work, a Hybrid Adaptive Differential Evolution and Pattern Search (hADE-PS) optimized Fractional Order Fuzzy PID (FOFPID) controller is suggested for AGC of power systems. At first, a non-reheat type two-area system is considered, and the improvement of the proposed approach over Bacteria Foraging Optimization Algorithm (BFOA), Teaching Learning Based Optimization (TLBO), Jaya Algorithm (JA), Genetic Algorithm (GA), and Hybrid BFOA and Particle Swarm Optimization Algorithm (hBFOA-PSO) for the identical power systems has been demonstrated. The AGC scheme was then extended to an interconnected reheat type power system and a two-area six-unit system. The results are compared with Firefly Algorithm (FA), Symbiotic Organism Search Algorithm (SOSA), and Artificial Bee Colony (ABC) for the second test system; and TLBO, Hybrid Stochastic Fractal Search and Local Unimodal Sampling (hSFS-LUS), ADE, and hADE-PS tuned PID for the third test system. Finally, the robustness of the suggested controller is examined under varied conditions. Examination of results confirms the improved performance of hADE-PS tuned FOFPID over other controllers with fewer error measures and better undershoot/overshoot/settling times of frequency/tie-line power deviations resulting from disturbances. To authenticate the viability of the recommended scheme, experimental validation using OPAL-RT based real-time simulation has been done.