This chapter is concerned with the stability enhancement of a power system using power system stabilizers (PSSs) designed based on four evolutionary algorithms (EAs), namely, genetic algorithms (GAs), breeder genetic algorithm (BGA), population-based incremental learning (PBIL), and differential evolution (DE). GAs have been widely applied in many fields of engineering and science and have shown to be a robust and powerful adaptive search algorithm. However, GAs are known to have several limitations. To deal with these limitations, many variant forms of GAs have been suggested often tailored to specific problems. In this research, we investigated the performances of GA-PSS and three other EAs-based PSSs (i.e., BGA-PSS and PBIL-PSS and DE-PSS) in improving the small-signal stability of a power system. These EAs have been selected on the basis of their simplicity, efficiency, and effectiveness in solving the optimization problem at hand. Frequency domain and time-domain simulation results show that DE-PSS, PBIL-PSS, and BGA-PSS performed better than GA-PSS. Time domain simulations suggest that overall, DE-PSS performs better than PBIL-PSS and BGA-PSS in terms of undershoot and subsequent swings, albeit with a relatively large first swing overshoot. The performances of BGA-PSS and PBIL-PSS are similar. On the other hand, GA-PSS gives a better response than the conventional PSS (CPSS).
Adequate damping is necessary to maintain the security and the reliability of power systems. The most-cost effective way to enhance the small-signal of a power system is to use power system controllers known as power system stabilizers (PSSs). In general, the parameters of these controllers are tuned using conventional control techniques such as root locus, phase compensation techniques, etc. However, with these methods, it is difficult to ensure adequate stability of the system over a wide range of operating conditions. Recently, there have been some attempts by researchers to use Evolutionary Algorithms (EAs) such as Genetic Algorithms (GAs), Particle Swarm Optimization, Differential Evolution (DE), etc., to optimally tune the parameters of the PSSs over a wide range of operating conditions. In this paper, a self-adaptive Differential Evolution (DE) is used to design a power system stabilizer for small-signal stability enhancement of a power system. By using self-adaptive DE, the control parameters of DE such as the mutation scale factor F and crossover rate CR are made adaptive as the population evolves. Simulation results are presented to show the effectiveness of the proposed approach.
The population size of DE plays a significant role in the way the algorithm performs as it influences whether good solutions can be found. Generally, the population size of DE algorithm is a user-defined input that remains fixed during the optimization process. Therefore, inadequate selection of DE population size may seriously hinder the performance of the algorithm. This paper investigates the impact of DE population size on (i) the performance of DE when applied to the optimal tuning of power system stabilizers (PSSs); and (ii) the ability of the tuned PSSs to perform efficiently to damp low-frequency oscillations. The effectiveness of these controllers is evaluated based on frequency domain analysis and validated using time-domain simulations. Simulation results show that a small population size may lead the algorithm to converge prematurely, and thus resulting in a poor controller performance. On the other hand, a large population size requires more computational effort, whilst no noticeable improvement in the performance of the controller is observed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.