Binary pathfinder algorithm for bus splitting optimisation problem

Dogan E., YÖRÜKEREN N.

IET GENERATION TRANSMISSION & DISTRIBUTION, vol.14, no.26, pp.6613-6638, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 14 Issue: 26
  • Publication Date: 2020
  • Doi Number: 10.1049/iet-gtd.2020.0729
  • Journal Name: IET GENERATION TRANSMISSION & DISTRIBUTION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.6613-6638
  • Kocaeli University Affiliated: Yes

Abstract

This study proposes a binary version of the pathfinder algorithm (BPFA) inspired by both the collective movement of animals and the leadership hierarchy of swarms in order to solve the existing bus splitting optimisation (BSO) problem encountered by transmission system operators. Some existing efficient algorithms, such as particle swarm optimisation, binary grey wolf optimiser, and salp swarm algorithms, are also utilised to obtain a suitable solution in the BSO problem and to compare it with the BPFA. The objective function of the BSO problem is built to restrict the short-circuit current and to provide the N - 1 security criteria. A new mathematical approach is proposed so as to assess the security performance of the transmission system during the solving of the BSO problem. The efficacy of the proposed algorithm is tested on well-known benchmark functions as well as IEEE 57 and IEEE 118 bus systems. Various transfer functions and position updating rules are implemented to each of the algorithms to acquire a better transition from continuous context to the binary format. The results obtained of the statistical indicators and the pairwise comparisons validate the efficiency and reliability of the BPFA algorithm in solving the compelling real-world problem.