Optimization of controllers using soft computing technique for load frequency control of multi-area deregulated power system

Abstract

Given the changing nature of power systems, it is challenging to optimize the controller for controlling load frequency problems. Distributed power generating sources and power system reorganization with multi-sources and multi-stakeholders make traditional load frequency control approaches unsuitable for current power systems. This research provides the comparative analysis of regulation of the load frequency in a multiple-area deregulated electricity system with the help of soft computing. In a reorganized electrical system, the major objectives of load frequency control (LFC) are to set up system frequency into acceptable limit, swiftly return the frequency to the setpoint, reduce tie-line power flow fluctuations across adjacent control zones, and track load demand agreements. To achieve LFC's goals, proportional integral derivative (PID) gain values must be tuned, for optimization purpose, soft computational methods are used in this present work. MATLAB/Simulink simulation results show that soft computing controllers can keep tie line power interchange within contracted constraints and frequency variation within the allowed range. This article compares auto tuned PID, genetic algorithm (GA), and particle swarm optimization (PSO) controllers in unregulated circumstances, load frequency regulation of two-area power systems.