Presentation Details
  1. Home
  2. Presentation List
  3. Presentation Details
Partial Discharge Fault Diagnosis of Switchgear Based on APSO-BP Algorithm
ID：140 View Protection：ATTENDEE Updated Time：2021-12-03 10:42:04 Hits：1077 Oral Presentation

Start Time：2021-12-15 15:30（Asia/Shanghai）

Duration：15min

Session：D High voltage and insulation technology » D1Session 4

Video No Permission Presentation File

Tips: Only the registered participant can access the file. Please sign in first.

Abstract
Diagnosis of partial discharge fault types of switchgear is the focus of the early warning of switchgear faults at this stage, which is of great significance to ensure the normal operation of the switchgear. Aiming at this problem, an adaptive particle swarm optimization (APSO) optimized BP neural network partial discharge fault diagnosis algorithm is proposed. By optimizing the inertia weight formula in the standard particle swarm, at the same time introducing genetic factors, mutation factors and time factors to accelerate the convergence speed of the particle swarm algorithm, so as to improve the performance of finding the optimal threshold and weight. First, the partial discharge signal is denoised, and then the signal features are extracted, and the dimensionality is reduced to 3-dimensional features through the principal component analysis algorithm, and finally the fault diagnosis is performed through the algorithm. Comparing the diagnosis results of different algorithms, it can be seen that the fault recognition rate of the APSO optimized BP neural network algorithm is about 5~15% higher than other algorithms, and the convergence speed and convergence accuracy are both improved, which proves the proposed APSO-BP algorithm Effectiveness.
Keywords
switchgear faults diagnosis; partial discharge; particle swarm algorithm; BP neural network;
Speaker
Zhuo Wang
Dalian Jiaotong University

I am currently studying for a master's degree at Dalian Jiaotong University. My main research field is high voltage detection technology.
 

Submission Author
Xiang Zheng Dalian Jiaotong University
Zhuo Wang Dalian Jiaotong University
Submit Comment
Verify Code Change Another
All Comments
Important Date

  • Conference Date

    Jul 11

    2023

    to

    Aug 18

    2023

  • Nov 10 2021

    Draft paper submission deadline

  • Dec 10 2021

    Registration deadline

  • Dec 11 2021

    Contribution Submission Deadline

Sponsored By
IEEE IAS
Organized By
IEEE IAS Student Chapter of Southwest Jiaotong University (SWJTU)
IEEE IAS Student Chapter of Huazhong University of Science and Technology (HUST)
IEEE PELS (Power Electronics Society) Student Chapter of HUST
Contact Information
Login to view full contact details
Previous Conferences