Computational intelligence based power tracing for nondiscriminatory losses charge allocation and voltage stability improvement. / Zulkifli Abdul Hamid

Abdul Hamid, Zulkifli (2013) Computational intelligence based power tracing for nondiscriminatory losses charge allocation and voltage stability improvement. / Zulkifli Abdul Hamid. In: The Doctoral Research Abstracts. IPSis Biannual Publication, 4 (4). Institute of Graduate Studies, UiTM, Shah Alam.

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Abstract

This thesis proposes a new power tracing technique using computational intelligence approach for nondiscriminatory losses charge allocation and voltage stability improvement. Contrary to conventional techniques which mainly rely on matrix operation, the proposed algorithm implements optimization technique as an alternative for performing the tracing process. At first, in producing a good optimization algorithm, a hybridization technique was proposed for adopting the finest features of two different algorithms; namely the Genetic Algorithm (GA) and continuous domain Ant Colony Optimization (ACOR). The hybrid algorithm is termed as the Blended Crossover Continuous Ant Colony Optimization (BX-CACO). It was found that performing power tracing via BX-CACO produced reliable tracing results as it is free from assumption like proportional sharing principle (PSP). Without treating the power system to be lossless, the tracing results are based on actual system condition; which means that they are consistent. Despite BX-CACO required computation time during optimization process, it is still within tolerable range. In addition, the proposed technique was able to promote fair losses charge allocation by involving imaginary consumers other than generation companies (GENCOs) and distribution companies (DISCOs); where, not all conventional tracing techniques include such consideration in their pricing scheme. Subsequently, the developed tracing algorithm was modified in the context of stability index tracing…

Item Type: Book Section
Creators:
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Abdul Hamid, ZulkifliUNSPECIFIED
Subjects: L Education > LB Theory and practice of education > Higher Education > Dissertations, Academic. Preparation of theses > Malaysia
Divisions: Institut Pengajian Siswazah (IPSis) : Institute of Graduate Studies (IGS)
Series Name: IPSis Biannual Publication
Volume: 4
Number: 4
Item ID: 19115
Uncontrolled Keywords: Abstract; Abstract of thesis; Newsletter; Research information; Doctoral graduates; IPSis; IGS; UiTM; Computational intelligence
Last Modified: 11 Jun 2018 01:39
Depositing User: Staf Pendigitalan 5
URI: http://ir.uitm.edu.my/id/eprint/19115

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