148 / 2024-08-31 18:03:00
Electrical Fault Diagnosis Based on Feature Extraction and Support Vector Machine for Permanent Magnet Synchronous Generator
permanent magnet synchronous generator,electrical fault,feature extraction,support vector machine
终稿
ZhangSichao / Xi’an Jiaotong University
ChenYu / Xi'an Jiaotong University
LiangFeng / Xi'an Jiaotong University
DuSiyu / Xi'an Jiaotong University
ShahbazNadeem / Xi’an Jiaotong University
ZhaoShouwang / Xi’an Jiaotong University
MaYong / Xi’an Thermal Power Research Institute Co. Ltd
DengWei / Xi’an Thermal Power Research Institute Co. Ltd
ZhaoYong / Xi’an Thermal Power Research Institute Co. Ltd
During the operation of the permanent magnet wind generator, electrical faults such as winding short circuit, winding open circuit, and winding asymmetry may occur, which directly affects the regular operation of the wind turbine and adversely affects wind power generation. This paper proposes an electrical fault diagnosis method for permanent magnet generators based on feature extraction and Support Vector Machine. By simulating electrical faults on a permanent magnet generator with a power of 25 kW, the 3-phase current and vibration signals of the generator are collected. Features are extracted from the current signal, and feature value classification is performed through support vector machine to implement pattern recognition of electrical faults and determine the operating status of the generator.
重要日期
  • 会议日期

    10月31日

    2024

    11月03日

    2024

  • 09月30日 2024

    初稿截稿日期

  • 11月12日 2024

    注册截止日期

主办单位
Anhui University
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
移动端
在手机上打开
小程序
打开微信小程序
客服
扫码或点此咨询