Fitness-distance balance based artificial ecosystem optimisation to solve transient stability constrained optimal power flow problem

Sonmez Y., Duman S., Kahraman H. T., Kati M., Aras S., Guvenc U.

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, vol.1, no.1, pp.1-40, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 1 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.1080/0952813x.2022.2104388
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Compendex, Computer & Applied Sciences, INSPEC, Psycinfo, zbMATH
  • Page Numbers: pp.1-40
  • Keywords: TSCOPF problem, modern power system, power system planning, Fitness-Distance Balance (FDB), artificial ecosystem optimisation, INTERIOR-POINT METHOD, BEE COLONY ALGORITHM, DIFFERENTIAL EVOLUTION, SECURITY
  • Karadeniz Technical University Affiliated: Yes


The Transient Stability Constrained Optimal Power Flow (TSCOPF) has become an important tool for power systems today. TSCOPF is a nonlinear optimisation problem, making its solution difficult, especially for small power systems. This paper presents a new optimisation method that incorporates Fitness-Distance Balance (FDB) with the Artificial Ecosystem Optimisation (AEO) algorithm to improve the solution quality in multi-dimensional and nonlinear optimisation problems. The proposed method, named the Fitness-Distance Balance Artificial Ecosystem Optimisation (FDBAEO), also has the capacity to solve the TSCOPF problem efficiently. In order to evaluate the proposed algorithm, it was tested on IEEE CEC benchmarks and on an IEEE 30-bus test system for the TSCOPF problem. Simulation results were compared with the basic AEO algorithm and other current meta-heuristic methods reported in the literature. The results showed that the proposed method was more effective in converging at the global optimum point in solving the TSCOPF problem compared to the other algorithms. This situation indicates that the design changes made in the decomposition phase of the AEO were more suitable for simulating the operation of the algorithm in the real world. The FDBAEO has exhibited a promising performance in solving both single-objective optimisation and constrained real-world engineering design problems.