Delta-Bar-Delta and directed random search algorithms to study capacitor banks switching overvoltages | Zendy

Iman Sadeghkhani | Zendy; Abbas Ketabi | Zendy; R. Feuillet | Zendy

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Delta-Bar-Delta and directed random search algorithms to study capacitor banks switching overvoltages

Author(s) -

Iman Sadeghkhani,

Abbas Ketabi,

R. Feuillet

Publication year - 2012

Publication title -

serbian journal of electrical engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.133

H-Index - 5

eISSN - 2217-7183

pISSN - 1451-4869

DOI - 10.2298/sjee1202217s

Subject(s) - artificial neural network , bar (unit) , delta , transient (computer programming) , engineering , capacitor , algorithm , electronic engineering , topology (electrical circuits) , artificial intelligence , computer science , electrical engineering , voltage , physics , meteorology , aerospace engineering , operating system

This paper introduces an approach to analyse transient overvoltages during capacitor banks switching based on artificial neural networks (ANN). Three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD) and directed random search (DRS) were used to train the ANNs. The ANN training is based on equivalent parameters of the network and therefore, a trained ANN is applicable to every studied system. The developed ANN is trained with extensive simulated results and tested for typical cases. The new algorithms are presented and demonstrated for a partial 39-bus New England test system. The simulated results show the proposed technique can accurately estimate the peak values of switching overvoltages

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