IEEE Trans Neural Netw Learn Syst - Improved Fault Classification in Series Compensated Transmission Line: Comparative Evaluation of Chebyshev Neural Network Training Algorithms.

Tópicos

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Resumo

This paper presents the Chebyshev neural network (ChNN) as an improved artificial intelligence technique for power system protection studies and examines the performances of two ChNN learning algorithms for fault classification of series compensated transmission line. The training algorithms are least-square Levenberg-Marquardt (LSLM) and recursive least-square algorithm with forgetting factor (RLSFF). The performances of these algorithms are assessed based on their generalization capability in relating the fault current parameters with an event of fault in the transmission line. The proposed algorithm is fast in response as it utilizes postfault samples of three phase currents measured at the relaying end corresponding to half-cycle duration only. After being trained with only a small part of the generated fault data, the algorithms have been tested over a large number of fault cases with wide variation of system and fault parameters. Based on the studies carried out in this paper, it has been found that although the RLSFF algorithm is faster for training the ChNN in the fault classification application for series compensated transmission lines, the LSLM algorithm has the best accuracy in testing. The results prove that the proposed ChNN-based method is accurate, fast, easy to design, and immune to the level of compensations. Thus, it is suitable for digital relaying applications.

Resumo Limpo

paper present chebyshev neural network chnn improv artifici intellig techniqu power system protect studi examin perform two chnn learn algorithm fault classif seri compens transmiss line train algorithm leastsquar levenbergmarquardt lslm recurs leastsquar algorithm forget factor rlsff perform algorithm assess base general capabl relat fault current paramet event fault transmiss line propos algorithm fast respons util postfault sampl three phase current measur relay end correspond halfcycl durat train small part generat fault data algorithm test larg number fault case wide variat system fault paramet base studi carri paper found although rlsff algorithm faster train chnn fault classif applic seri compens transmiss line lslm algorithm best accuraci test result prove propos chnnbase method accur fast easi design immun level compens thus suitabl digit relay applic

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