Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/12219
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dc.contributor.advisorWang, Z-
dc.contributor.advisorLiu, X-
dc.contributor.authorRahman, Izaz Ur-
dc.date.accessioned2016-02-29T16:21:17Z-
dc.date.available2016-02-29T16:21:17Z-
dc.date.issued2016-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/12219-
dc.descriptionThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University Londonen_US
dc.description.abstractOptimization problems are vital in physical sciences, commercial and finance matters. In a nutshell, almost everyone is the stake-holder in certain optimization problems aiming at minimizing the cost of production and losses of system, and also maximizing the profit. In control systems, the optimal configuration problems are essential that have been solved by various newly developed methods. The literature is exhaustively explored for an appropriate optimization method to solve such kind of problems. Particle Swarm Optimization is found to be one of the best among several optimization methods by analysing the experimental results. Two novel PSO variants are introduced in this thesis. The first one is named as N State Markov Jumping Particle Swarm Optimization, which is based on the stochastic technique and Markov chain in updating the particle velocity. We have named the second variant as N State Switching Particle Swarm Optimization, which is based on the evolutionary factor information for updating the velocity. The proposed algorithms are then applied to some widely used mathematical benchmark functions. The statistical results of 30 independent trails illustrate the robustness and accuracy of the proposed algorithms for most of the benchmark functions. The better results in terms of mean minimum evaluation errors and the shortest computation time are illustrated. In order to verify the satisfactory performance and robustness of the proposed algorithms, we have further formulated some basic applications in power system operations. The first application is about the static Economic Load Dispatch and the second application is on the Dynamic Economic Load Dispatch. These are highly complex and non-linear problems of power system operations consisting of various systems and generator constraints. Basically, in the static Economic Load Dispatch, a single load is considered for calculating the cost function. In contrast, the Dynamic Economic Load Dispatch changes the load demand for the cost function dynamically with time. In such a challenging and complex environment the proposed algorithms can be applied. The empirical results obtained by applying both of the proposed methods have substantiated their adaptability and robustness into the real-world environment. It is shown in the numerical results that the proposed algorithms are robust and accurate as compared to the other algorithms. The proposed algorithms have produced consistent best values for their objectives, where satisfying all constraints with zero penalty.en_US
dc.language.isoenen_US
dc.publisherBrunel University Londonen_US
dc.relation.urihttp://bura.brunel.ac.uk/bitstream/2438/12219/1/FulltextThesis.pdf-
dc.subjectOptimization in power systemsen_US
dc.subjectEvolutionary algorithms in smart griden_US
dc.subjectEvolutionary computation in power systemsen_US
dc.subjectparticle swarm optimisation in power systemsen_US
dc.titleNovel particle swarm optimization algorithms with applications in power systemsen_US
dc.typeThesisen_US
Appears in Collections:Computer Science
Dept of Computer Science Theses

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