Power disturbance recognition using back-propagation neural networks

研究成果: Paper

2 引文 斯高帕斯(Scopus)

摘要

This paper presents power disturbance recognition using back-propagation neural networks (BPNN). First, the discrete wavelet transform is used to extract the features of the power disturbance waveforms in the form of series coefficients of several levels. The Parseval theory is then utilized to calculate the energy of each level so that the number of coefficients can be reduced; then, the extracted results are used for recognition by the BPNN. Multi-event power disturbances are also fed to the recognition system for testing. From experiment results, the recognition rate is at least 83.67%. It proves the feasibility of the proposed method.

原文English
頁面430-433
頁數4
出版狀態Published - 2012 一月 1
事件2012 International Workshop on Computer Science and Engineering, WCSE 2012 - Hong Kong, Hong Kong
持續時間: 2012 八月 32012 八月 4

Conference

Conference2012 International Workshop on Computer Science and Engineering, WCSE 2012
國家Hong Kong
城市Hong Kong
期間12-08-0312-08-04

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering(all)

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  • 引用此

    Wang, C. S. (2012). Power disturbance recognition using back-propagation neural networks. 430-433. 論文發表於 2012 International Workshop on Computer Science and Engineering, WCSE 2012, Hong Kong, Hong Kong.