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dc.contributor.authorDash, P K-
dc.contributor.authorPanda, S K-
dc.contributor.authorLiew, A C-
dc.contributor.authorMishra, B-
dc.contributor.authorJena, R K-
dc.identifier.citationElectric Power Systems Research, Vol 46, Iss 1, P 11-20en
dc.descriptionCopyright for this article belongs to Elsevier Science Ltden
dc.description.abstractThe paper presents an adaptive neural network approach for the estimation of harmonic distortions and power quality in power networks. The neural estimator is based on the use of linear adaptive neural elements called adalines The learning parameter of the proposed algorithm is suitably adjusted to provide fast convergence and noise rejection for tracking distorted signals in the power networks. Several numerical tests have been conducted for the adaptive estimation of harmonic components, total harmonic distortions, power quality of simulated waveforms in power networks supplying converter loads and switched capacitors. Laboratory test results are also presented in support of the performance of the new algorithm.en
dc.format.extent338275 bytes-
dc.subjectNeural networken
dc.subjectHarmonic distortionsen
dc.subjectElectric power systemsen
dc.titleA new approach to monitoring electric power qualityen
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