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Analysis of power quality disturbance based on improved HHT and BPSO-SVM
ID：270 View Protection：ATTENDEE Updated Time：2022-05-19 12:57:11 Hits：310 Poster Presentation

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Abstract
Abstract—Aiming at the problems of complex feature extraction and low classification accuracy of traditional composite PQD classification methods. This paper combines I. Hilbert-Huang transform (HHT) and Binary Particle Swarm Optimization- Support Vector Machine (BPSO-SVM) to analyze PDQ. The different disturbance components in the original signal are decomposed into different IMF components by EMD decomposition, so as to transform the multi classification problem into a binary classification problem to improve the classification efficiency. Aiming at the problem of modal mixing in the process of EMD decomposition, the frequency information in the same octave that is far apart in the same octave is stripped by adding a preset correction signal to the signal in the decomposition process. The simulation results of Matlab show that the accuracy of the compound disturbance classification is significantly improved.
 
Keywords
power quality disturbance; SVM; BPSO; Hilbert-Huang transform
Speaker
WangJiakai
长安大学

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Important Date

  • Conference Date

    May 27

    2022

    to

    May 29

    2022

  • Feb 28 2022

    Draft paper submission deadline

  • May 29 2022

    Registration deadline

  • Jun 22 2022

    Contribution Submission Deadline

Sponsored By
IEEE Beijing Section
China Electrotechnical Society
Southeast University
Supported By
IEEE Industry Applications Society
IEEE Nanjing Section
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