Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31843
Title: Wavelet feature vectors for neural network based harmonics load recognition
Authors: Chan, WL 
So, TP
Lai, LL
Keywords: Harmonic distortion
Load (electric)
Neural nets
Power supply quality
Power system analysis computing
Power system harmonics
Vectors
Wavelet transforms
Issue Date: 2000
Publisher: IET
Source: 2000 International Conference on Advances in Power System Control, Operation and Management, 2000 : APSCOM-00, 30 October-1 November 2000, v. 2, p. 511-516 How to cite?
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, the authors 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.
URI: http://hdl.handle.net/10397/31843
ISBN: 0-85296-791-8
DOI: 10.1049/cp:20000453
Appears in Collections:Conference Paper

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