Wavelet feature vectors for neural network based harmonics load recognition

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

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

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.
Original languageEnglish
Title of host publication2000 International Conference on Advances in Power System Control, Operation and Management, 2000 : APSCOM-00, 30 October-1 November 2000
PublisherInstitution of Engineering and Technology
Pages511-516
Number of pages6
ISBN (Print)0852967918
DOIs
Publication statusPublished - 2000
EventInternational Conference on Advances in Power System Control, Operation and Management [APSCOM] -
Duration: 1 Jan 2000 → …

Conference

ConferenceInternational Conference on Advances in Power System Control, Operation and Management [APSCOM]
Period1/01/00 → …

Keywords

  • Harmonic distortion
  • Load (electric)
  • Neural nets
  • Power supply quality
  • Power system analysis computing
  • Power system harmonics
  • Vectors
  • Wavelet transforms

Cite this