Wavelet feature vectors for neural network based harmonics load recognition

Wai Lok Chan, A. T.P. So, L. L. Lai

Research output: Journal article publicationConference articleAcademic researchpeer-review

14 Citations (Scopus)

Abstract

Power quality embraces problems caused by harmonics, over or under-voltages, or supply discontinuities. Harmonics are caused by all sorts of non-linear loads. In order to fully understand the problems, an effective means of identifying sources of power harmonics is important. In this paper, we make use of new developments in wavelets so that each type of current waveform polluted with power harmonics can well be represented by a normalised energy vector consisting of five elements. Furthermore, a mixture of harmonics load can also be represented by a corresponding vector. This paper describes the mathematics and algorithms for arriving at the vectors, forming a strong foundation for real-time harmonics signature recognition, in particular, useful to the re-structuring of the whole electric power industry. The system performs exceptionally well with the aid of an artificial neural network.
Original languageEnglish
Pages (from-to)511-516
Number of pages6
JournalIEE Conference Publication
Issue number478 II
Publication statusPublished - 1 Dec 2001
Event5th International Conference on Advances in Power System Control, Operation and Management - Tsimshatsui, Kowloon, Hong Kong
Duration: 30 Oct 20001 Nov 2000

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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