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Muhammad Affiq Abd Rahman
Universiti Kuala Lumpur British Malaysian Institute

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Review on population-based metaheuristic search techniques for optimal power flow Muhammad Affiq Abd Rahman; Bazilah Ismail; Kanendra Naidu; Mohd Khairil Rahmat
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 1: July 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i1.pp373-381

Abstract

Optimal power flow (OPF) is a non-linear solution which is significantly important in order to analyze the power system operation. The use of optimization algorithm is essential in order to solve OPF problems. The emergence of machine learning presents further techniques which capable to solve the non-linear problem. The performance and the key aspects which enhances the effectiveness of these optimization techniques are compared within several metaheuristic search techniques. This includes the operation of particle swarm optimization (PSO) algorithm, firefly algorithm (FA), artificial bee colony (ABC) algorithm, ant colony optimization (ACO) algorithm and differential evolution (DE) algorithm. This paper reviews on the key elements that need to be considered when selecting metaheuristic techniques to solve OPF problem in power system operation.
Co-Authors Bazilah Ismail Kanendra Naidu Mohd Khairil Rahmat