Differential evolution solution to transformer no-load loss reduction problem

Georgilakis, PS

dc.contributor.author	Georgilakis, PS	en
dc.date.accessioned	2014-03-01T01:30:12Z
dc.date.available	2014-03-01T01:30:12Z
dc.date.issued	2009	en
dc.identifier.issn	1751-8687	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19495
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Active parts	en
dc.subject.other	Combinatorial optimisation	en
dc.subject.other	Core manufacturing	en
dc.subject.other	Differential Evolution	en
dc.subject.other	Distribution transformer	en
dc.subject.other	Grouping process	en
dc.subject.other	Load loss	en
dc.subject.other	Multi-layer perceptrons	en
dc.subject.other	New approaches	en
dc.subject.other	Population diversity	en
dc.subject.other	Scaling factors	en
dc.subject.other	Wound core	en
dc.subject.other	Combinatorial optimization	en
dc.subject.other	Electric load loss	en
dc.subject.other	Evolutionary algorithms	en
dc.subject.other	Integrodifferential equations	en
dc.subject.other	Pattern recognition systems	en
dc.subject.other	Problem solving	en
dc.title	Differential evolution solution to transformer no-load loss reduction problem	en
heal.type	journalArticle	en
heal.identifier.primary	10.1049/iet-gtd.2009.0184	en
heal.identifier.secondary	http://dx.doi.org/10.1049/iet-gtd.2009.0184	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	After the completion of core manufacturing and before the assembly of transformer active part, 2N small individual cores and 2N large individual cores are available and have to be optimally combined into N transformers so as to minimise the total no-load loss (NLL) of N transformers. This complex combinatorial optimisation problem is called transformer no-load loss reduction (TNLLR) problem. A new approach combining differential evolution (DE) and multilayer perceptrons (MLPs) to solve TNLLR problem is proposed. MLPs are used to predict NLL of wound core distribution transformers. An improved differential evolution (IDE) method is proposed for the solution of TNLLR problem. The modifications of IDE in comparison to the simple DE method are (i) the scaling factor F is varied randomly within some range, (ii) an auxiliary set is employed to enhance the population diversity, (iii) the newly generated trial vector is compared with the nearest parent and (iv) the simple feasibility rule is used to treat the constraints. Application results show that the performance of the proposed method is better than that of two other methods, that is, conventional grouping process and genetic algorithm. Moreover, the proposed method provides 7.3 reduction in the cost of transformer main materials. © The Institution of Engineering and Technology.	en
heal.publisher	INST ENGINEERING TECHNOLOGY-IET	en
heal.journalName	IET Generation, Transmission and Distribution	en
dc.identifier.doi	10.1049/iet-gtd.2009.0184	en
dc.identifier.isi	ISI:000270789900008	en
dc.identifier.volume	3	en
dc.identifier.issue	10	en
dc.identifier.spage	960	en
dc.identifier.epage	969	en