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Intelligent Building Fault Diagnosis Based on Wavelet Transform and Bayesian Network

  • Jundong Fu
  • Luming Huang
  • Li Chen
  • Yunxia Qiu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 890)

Abstract

A novel fault diagnosis method is proposed in this paper in the distribution network based on wavelet transform and a Bayesian network. After the wavelet transform, decomposition, and reconstruction of various electrical basic quantities by amplitude, phase angle, and energy, the electrical fault feature quantity is combined according to various weights, and then the corresponding component switch fault characteristics are calculated by Bayesian. A simple Bayesian fusion of the electrical fault feature and component switch fault characteristics is used as the eigenvector of the Bayesian network, and then trained and predicted by Bayesian network. The experimental simulation results show that the fault diagnosis method for power distribution network based on wavelet transform and Bayesian network proposed in this paper has an obvious recognition degree according to the single fault feature. It is very accurate to identify the type and faulty components.

Keywords

Intelligent building Fault diagnosis Pattern recognition Bayesian network Wavelet transform 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Jundong Fu
    • 1
  • Luming Huang
    • 1
  • Li Chen
    • 1
  • Yunxia Qiu
    • 1
  1. 1.School of Electrical and Automation EngineeringEast China Jiaotong UniversityNanchangChina

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