Application of improved particle swarm optimization in economic dispatch of power systems
Economic dispatch is an important optimization challenge in power systems. It helps to find the optimal output power of a number of generating units that satisfy the system load demand at the cheapest cost, considering equality and inequality constraints. Many nature inspired algorithms have been br...
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Online Access: | Gninkeu Tchapda, Ghislain Yanick (2018) Application of improved particle swarm optimization in economic dispatch of power systems, University of South Africa, Pretoria, <http://hdl.handle.net/10500/24428> http://hdl.handle.net/10500/24428 |
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ndltd-netd.ac.za-oai-union.ndltd.org-unisa-oai-uir.unisa.ac.za-10500-244282018-11-19T17:16:01Z Application of improved particle swarm optimization in economic dispatch of power systems Gninkeu Tchapda, Ghislain Yanick Wang, Z. Particle swarm optimization Economic dispatch Swarm intelligence Genetic algorithm Evolutionary algorithm Bat algorithm Cuckoo search algorithm Power systems Levy flight Random search Thermal power plant 621.31 Swarm intelligence Genetic algorithms Electric power systems Economic dispatch is an important optimization challenge in power systems. It helps to find the optimal output power of a number of generating units that satisfy the system load demand at the cheapest cost, considering equality and inequality constraints. Many nature inspired algorithms have been broadly applied to tackle it such as particle swarm optimization. In this dissertation, two improved particle swarm optimization techniques are proposed to solve economic dispatch problems. The first is a hybrid technique with Bat algorithm. Particle swarm optimization as the main optimizer integrates bat algorithm in order to boost its velocity and to adjust the improved solution. The second proposed approach is based on Cuckoo operations. Cuckoo search algorithm is a robust and powerful technique to solve optimization problems. The study investigates the effect of levy flight and random search operation in Cuckoo search in order to ameliorate the performance of the particle swarm optimization algorithm. The two improved particle swarm algorithms are firstly tested on a range of 10 standard benchmark functions and then applied to five different cases of economic dispatch problems comprising 6, 13, 15, 40 and 140 generating units. Electrical and Mining Engineering M. Tech. (Electrical Engineering) 2018-06-27T09:20:25Z 2018-06-27T09:20:25Z 2018-03 2018-06 Dissertation Gninkeu Tchapda, Ghislain Yanick (2018) Application of improved particle swarm optimization in economic dispatch of power systems, University of South Africa, Pretoria, <http://hdl.handle.net/10500/24428> http://hdl.handle.net/10500/24428 en 1 online resource (xiv, 80 leaves) : illustrations (chiefly color), graphs (chiefly color) |
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Particle swarm optimization Economic dispatch Swarm intelligence Genetic algorithm Evolutionary algorithm Bat algorithm Cuckoo search algorithm Power systems Levy flight Random search Thermal power plant 621.31 Swarm intelligence Genetic algorithms Electric power systems |
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Particle swarm optimization Economic dispatch Swarm intelligence Genetic algorithm Evolutionary algorithm Bat algorithm Cuckoo search algorithm Power systems Levy flight Random search Thermal power plant 621.31 Swarm intelligence Genetic algorithms Electric power systems Gninkeu Tchapda, Ghislain Yanick Application of improved particle swarm optimization in economic dispatch of power systems |
description |
Economic dispatch is an important optimization challenge in power systems. It helps to find the optimal output power of a number of generating units that satisfy the system load demand at the cheapest cost, considering equality and inequality constraints. Many nature inspired algorithms have been broadly applied to tackle it such as particle swarm optimization. In this dissertation, two improved particle swarm optimization techniques are proposed to solve economic dispatch problems. The first is a hybrid technique with Bat algorithm. Particle swarm optimization as the main optimizer integrates bat algorithm in order to boost its velocity and to adjust the improved solution. The second proposed approach is based on Cuckoo operations. Cuckoo search algorithm is a robust and powerful technique to solve optimization problems. The study investigates the effect of levy flight and random search operation in Cuckoo search in order to ameliorate the performance of the particle swarm optimization algorithm. The two improved particle swarm algorithms are firstly tested on a range of 10 standard benchmark functions and then applied to five different cases of economic dispatch problems comprising 6, 13, 15, 40 and 140 generating units. === Electrical and Mining Engineering === M. Tech. (Electrical Engineering) |
author2 |
Wang, Z. |
author_facet |
Wang, Z. Gninkeu Tchapda, Ghislain Yanick |
author |
Gninkeu Tchapda, Ghislain Yanick |
author_sort |
Gninkeu Tchapda, Ghislain Yanick |
title |
Application of improved particle swarm optimization in economic dispatch of power systems |
title_short |
Application of improved particle swarm optimization in economic dispatch of power systems |
title_full |
Application of improved particle swarm optimization in economic dispatch of power systems |
title_fullStr |
Application of improved particle swarm optimization in economic dispatch of power systems |
title_full_unstemmed |
Application of improved particle swarm optimization in economic dispatch of power systems |
title_sort |
application of improved particle swarm optimization in economic dispatch of power systems |
publishDate |
2018 |
url |
Gninkeu Tchapda, Ghislain Yanick (2018) Application of improved particle swarm optimization in economic dispatch of power systems, University of South Africa, Pretoria, <http://hdl.handle.net/10500/24428> http://hdl.handle.net/10500/24428 |
work_keys_str_mv |
AT gninkeutchapdaghislainyanick applicationofimprovedparticleswarmoptimizationineconomicdispatchofpowersystems |
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1718795035277787136 |