Robust load frequency control using genetic algorithms and linear matrix inequalities

Abstract

In this paper, two robust decentralized control design methodologies for load frequency control (LFC) are proposed. The first one is based on control design using linear matrix inequalities (LMI) technique in order to obtain robustness against uncertainties. The second controller has a simpler structure, which is more appealing from an implementation point of view, and it is tuned by a proposed novel robust control design algorithm to achieve the same robust performance as the first one. More specifically, genetic algorithms (GAs) optimization is used to tune the control parameters of the proportional-integral (PI) controller subject to the constraints in terms of LMI. Hence, the second control design is called GALMI. Both proposed controllers are tested on a three-area power system with three scenarios of load disturbances to demonstrate their robust performances.

A robust decentralized load frequency controller for interconnected power systems

“…proposed in [11]. It includes nine similar non-reheat turbine units that are evenly distributed in the three interconnected areas respectively.…”

“…The T s is much shorter than the one in the PID controlled system in [9][10][11][12][13]. The responses of area 3 have relatively large overshoots compared to the other two areas.…”

“…The dynamic model of area 1 is shown in Fig. 10, which is similar to the model in [11], where the GALMI tuned PI controller was proposed. In Fig.…”

“…10, the parameters M area1 (inertia constant for area 1) and D area1 (load damping constant for area 1) are equivalent to the parameters ''M'' and ''D'' in [11] for the generator model. We choose a system model similar to the one in [11] for the comparison of the performances between the GALMI tuned PI controller and ADRC controller.…”

“…Among various types of decentralized lFCs. the most widely em ployed in power industry is PID control [8][9][10][11][12][13] [. The PI con troller tuned through genetic algorithm linear matrix inequalities (GAlMls) [ 11 1 becomes increasingly popular in recent years.…”