Load Frequency Control in Power Systems Using Multi Objective Genetic Algorithm & Fuzzy Sliding Mode Control

This study proposes a combination of a fuzzy sliding mode controller (FSMC) with integral-proportion-Derivative switching surface based superconducting magnetic energy storage (SMES) and PID tuned by a multi-objective optimization algorithm to solve the load frequency control in power systems. The goal of design is to improve the dynamic response of power systems after load demand changes. In the proposed method, an adaptive fuzzy controller is utilized to mimic a feedback linearization control law. To compensate the compensation error between the feedback linearization and adaptive fuzzy controller, a hitting controller is developed. The Lyapunov stability theory is used to obtain an adaption law so that the closed-loop system stability can be guaranteed. The optimal PID controller problem is formulated into a multi-objective optimization problem. A Pareto set of global optimal solutions to the given multi-objective optimization problem is generated by a genetic algorithm (GA)-based solution technique. The best compromise solution from the generated Pareto solution set is selected by using a fuzzy-based membership value assignment method. Simulations are presented and compared with conventional PID controller and another new controller. These results demonstrate that the proposed controller confirms better disturbance rejection, keeps the control quality in the wider operating range, reduces the frequency’s transient response avoiding the overshoot and is more robust to uncertainties in the system.