Optimal allocation of FACTS devices for static security enhancement in power systems via imperialistic competitive algorithm (ICA)

• Published in: Applied soft computing Vol. 48; pp. 317 - 328
• Main Author: Jordehi, A. Rezaee
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2016
• Subjects: FACTS allocation problem; Imperialistic competitive algorithm; Optimisation; Thyristor-controlled phase shifting transformer; Thyristor-controlled series compensator; Transmission system; Thyristor-controlled series compensator; Transmission system; Imperialistic competitive algorithm; Optimisation; Thyristor-controlled phase shifting transformer; FACTS allocation problem
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2016.07.014

[Display omitted] •This paper puts forward ICA for allocating FACTS devices.•TCPST and TCSC are used to relieve consequences of line outage and increased demand.•The results show that ICA efficiently solves TCPST and TCSC allocation problems.•The results show that FACTS devices drastically enhance power system static security.•The results approve the outperformance of ICA over some state of the art algorithms. The problem of optimal allocation of flexible AC transmission systems (FACTS) devices is deemed as a formidable optimisation problem. Metaheuristics are the common approaches for solving FACTS allocation problems. Imperialistic competitive algorithm (ICA) is a well-established optimisation algorithm which has been successfully employed for solving complex optimisation problems in different fields. It is inspired by imperialistic competition and socio-political evolution of human beings and offers appropriate exploration and exploitation capabilities during the search for global optima. This paper employs ICA for solving FACTS allocation problem in a way that low values of overloads and voltage deviations are resulted both during line outage contingencies and demand growth. Thyristor-controlled phase shifting transformers (TCPST’s) and thyristor-controlled series compensators (TCSC’s) have been used as FACTS devices. The results of employing ICA for FACTS allocation problem indicate that ICA Offers better results than artificial bee colony (ABC), gravitational search algorithm (GSA), evolutionary programming (EP), bat swarm optimisation (BSO), nonlinear programming (NLP), pattern search (PS), asexual reproduction optimisation (ARO) and backtracking search algorithm (BSA).