Open AccessNew approach to design SVC‐based stabiliser using genetic algorithm and rough set theory
Author(s) -
Fetouh Tamer,
Zaky Mohamed S.
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2016.0701
Subject(s) - rough set , genetic algorithm , scalability , set (abstract data type) , computer science , controller (irrigation) , electric power system , process (computing) , stabiliser , reduction (mathematics) , stability (learning theory) , power (physics) , algorithm , control theory (sociology) , mathematical optimization , data mining , control (management) , artificial intelligence , machine learning , mathematics , chemistry , physics , geometry , food science , quantum mechanics , database , biology , agronomy , programming language , operating system
A new approach for coordinated design of a static VAR compensator‐based stabiliser and a conventional power system stabiliser is proposed. The approach is based on the integration between genetic algorithm (GA) and rough‐set theory. The role of rough set is to select the most dominant controller parameters that are involved in the optimisation process. The proposed approach aims to minimise the computational time and reduce the storage capacity required for the optimisation problem as well as improve the performance of power system stability of power system. The proposed rough‐set‐based GA is applied to select the controller parameters included in the optimisation process as well as search for their optimal setting. This study also presents a comparison between the system performances when utilising individual or coordinated controllers with those of system utilising the proposed approach. Single machine system is used to investigate the efficacy of the proposed approach and multi‐machine system is used to demonstrate the applicability and scalability of the proposed method. The simulation results and comparison analysis show the effectiveness of the rough‐set‐based GA. In addition, a good reduction in optimisation time and size of information is achieved by applying the rough‐set‐based GA.