Fault Diagnosis and Identification of Power Capacitors Based on Cloud Computing and Depth Study

HUANG Yuchun; CAO Chengtao; GU Hai

doi:10.14044/j.1674-1757.pcrpc.2018.04.013

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Fault Diagnosis and Identification of Power Capacitors Based on Cloud Computing and Depth Study

更新时间：2024-09-14

- Fault Diagnosis and Identification of Power Capacitors Based on Cloud Computing and Depth Study
- POWER CAPACITOR & REACTIVE POWER COMPENSATION Vol. 39, Issue 4, Pages: 71-75(2018)
- 作者机构：
  
  1. 国网河南省电力公司漯河供电公司
  2. 华南理工大学
  3. 哈尔滨工业大学
- 作者简介：
- 基金信息：
- DOI：10.14044/j.1674-1757.pcrpc.2018.04.013
  CLC： TM53
- Published：2018，
- 稿件说明：
移动端阅览
[1]黄予春,曹成涛,顾海.基于云计算和深度学习的电力电容器故障诊断和识别[J].电力电容器与无功补偿,2018,39(04):71-75.

HUANG YUCHUN, CAO CHENGTAO, GU HAI. Fault Diagnosis and Identification of Power Capacitors Based on Cloud Computing and Depth Study. [J]. Power capacitor & reactive power compensation, 2018, 39(4): 71-75.
[1]黄予春,曹成涛,顾海.基于云计算和深度学习的电力电容器故障诊断和识别[J].电力电容器与无功补偿,2018,39(04):71-75. DOI： 10.14044/j.1674-1757.pcrpc.2018.04.013.

HUANG YUCHUN, CAO CHENGTAO, GU HAI. Fault Diagnosis and Identification of Power Capacitors Based on Cloud Computing and Depth Study. [J]. Power capacitor & reactive power compensation, 2018, 39(4): 71-75. DOI： 10.14044/j.1674-1757.pcrpc.2018.04.013.

摘要

为实现电力电容器故障诊断和识别的高精度预测

将云计算技术引入深度学习

提出一种基于Map Reduce的分布式DBN的电力电容器故障诊断和识别方法。通过MR

BN和DBN、SVM、BP的对比

研究结果表明

BN可提高电力电容器故障诊断和识别的精度

精度可达99.41%

从而为电力电容器故障的研究和应用提供了新的方法。

Abstract

For achieving high accuracy prediction of fault diagnosis and identification of power capacitor

and introducing cloud computing technology into depth study

a kind of fault diagnosis and identification method of power capacitor based on Map Reduce distribution DBN is proposed. It is shown by the study result by way of comparison of MR

BN and DBN

SVM and BP that MR

BN can improve the accuracy of fault diagnosis and identification of power capacitor up to 99.41%

thus providing new method for the study and application of the fault of power capacitor.

关键词

云计算深度学习故障诊断电力电容器支持向量机

Keywords

cloud computingdepth studyfault diagnosispower capacitorssupport vector machine

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Related Institution

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