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Hybridization of particle Swarm Optimization with Bat Algorithm for optimal reactive power dispatch

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dc.contributor.advisor Wang, Zenghui
dc.contributor.author Agbugba, Emmanuel Emenike
dc.date.accessioned 2018-02-27T12:31:42Z
dc.date.available 2018-02-27T12:31:42Z
dc.date.issued 2017-06
dc.identifier.citation Agbugba, Emmanuel Emenike (2017) Hybridization of particle Swarm Optimization with Bat Algorithm for optimal reactive power dispatch, University of South Africa, Pretoria, <http://hdl.handle.net/10500/23630>
dc.identifier.uri http://hdl.handle.net/10500/23630
dc.description.abstract This research presents a Hybrid Particle Swarm Optimization with Bat Algorithm (HPSOBA) based approach to solve Optimal Reactive Power Dispatch (ORPD) problem. The primary objective of this project is minimization of the active power transmission losses by optimally setting the control variables within their limits and at the same time making sure that the equality and inequality constraints are not violated. Particle Swarm Optimization (PSO) and Bat Algorithm (BA) algorithms which are nature-inspired algorithms have become potential options to solving very difficult optimization problems like ORPD. Although PSO requires high computational time, it converges quickly; while BA requires less computational time and has the ability of switching automatically from exploration to exploitation when the optimality is imminent. This research integrated the respective advantages of PSO and BA algorithms to form a hybrid tool denoted as HPSOBA algorithm. HPSOBA combines the fast convergence ability of PSO with the less computation time ability of BA algorithm to get a better optimal solution by incorporating the BA’s frequency into the PSO velocity equation in order to control the pace. The HPSOBA, PSO and BA algorithms were implemented using MATLAB programming language and tested on three (3) benchmark test functions (Griewank, Rastrigin and Schwefel) and on IEEE 30- and 118-bus test systems to solve for ORPD without DG unit. A modified IEEE 30-bus test system was further used to validate the proposed hybrid algorithm to solve for optimal placement of DG unit for active power transmission line loss minimization. By comparison, HPSOBA algorithm results proved to be superior to those of the PSO and BA methods. In order to check if there will be a further improvement on the performance of the HPSOBA, the HPSOBA was further modified by embedding three new modifications to form a modified Hybrid approach denoted as MHPSOBA. This MHPSOBA was validated using IEEE 30-bus test system to solve ORPD problem and the results show that the HPSOBA algorithm outperforms the modified version (MHPSOBA). en
dc.format.extent 1 online resource (xiii, 128 leaves) illustrations (some color) en
dc.language.iso en en
dc.subject Hybridization en
dc.subject Hybrid Particle Swarm Optimization (HPSOBA) en
dc.subject Modified Hybrid Particle Swarm Optimization (MHPSOBA) en
dc.subject Optimal Power Flow (OPF) en
dc.subject Optimal Reactive Power Dispatch (ORPD) en
dc.subject Particle Swarm Optimization (PSO) en
dc.subject Bat Algorithm (BA) en
dc.subject Active power loss minimization en
dc.subject Benchmark functions en
dc.subject Distributed Generation (DG) en
dc.subject Conventional optimization technique en
dc.subject Evolutionary optimization technique en
dc.subject Artificial intelligence en
dc.subject Equality constraints en
dc.subject Inequality constraints en
dc.subject Penalty function en
dc.subject.ddc 620.00151
dc.subject.lcsh Mathematical optimization en
dc.subject.lcsh Swarm intelligence en
dc.subject.lcsh Engineering mathematics -- Data processing en
dc.subject.lcsh MATLAB en
dc.subject.lcsh Artificial intelligence en
dc.subject.lcsh Computer-aided engineering en
dc.title Hybridization of particle Swarm Optimization with Bat Algorithm for optimal reactive power dispatch en
dc.type Dissertation en
dc.description.department Electrical and Mining Engineering en
dc.description.degree M. Tech. (Electrical Engineering) en


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  • Unisa ETD [12560]
    Electronic versions of theses and dissertations submitted to Unisa since 2003

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