Intelligent Linear-Quadratic Optimal Output Feedback Regulator for a Deregulated Automatic Generation Control System

Abstract

One of the main observed problems in the control of automatic generation control systems is the limitation to access and measurement of state variables in the real world. In order to solve this problem, an optimal output feedback method, the linear-quadratic regulator controller, is used. In the output feedback method, only measurable state variables within each control area are required to use for feedback. But in order to improve dynamic performance and provide a better design for this controller, the concept of an intelligent regulator is added to the linear-quadratic regulators; as a result, the particle swarm optimization based linear-quadratic output feedback regulator and the imperialist competitive algorithm based linear-quadratic output feedback are proposed to calculate the global optimal gain matrix of controller intelligently. The optimal control law of this controller must be determined by minimizing a performance index under the output feedback conditions leading to coupled matrix equations. In conventional methods, the control law is handled by pole placement, iterative, or trial-and-error methods for choosing controller gains; thus, intelligent optimization techniques are applied to solve this problem. The proposed controllers are tested on a two-area automatic generation control power system, and a complete comparison between the proposed output feedback controllers with adaptive weighted particle swarm optimization, particle swarm optimization, the imperialist competitive algorithm, and a conventional output feedback controller is presented. The results show that the proposed intelligent controller improved the dynamic response of the system faster than the conventional controller and provided a control system that satisfied the load frequency control requirements.