机电工程技术2025，Vol.54Issue(10)：164-167,4.DOI:10.3969/j.issn.1009-9492.2025.10.034
基于半监督SVM模型的新能源发电成套电力设备故障诊断方法
Fault Diagnosis Method of Complete Power Equipment for New Energy Generation Based on Semi-supervised SVM Model
董鹏程 1阎凯1
作者信息
- 1. 天津军粮城发电有限公司,天津 300300
- 折叠
摘要
Abstract
A new method for fault diagnosis based on semi supervised SVM model is proposed to solve the problem of insufficient labeled data in the fault diagnosis of new energy generation complete power equipment.This method aims to achieve efficient and accurate diagnosis of power equipment fault status through limited annotated data.Firstly,a semi supervised SVM model is used to perform in-depth feature extraction on the status monitoring data of power equipment,in order to accurately obtain the key indicator parameters of equipment status.Based on these key parameters,further calculate the reliability indicators of equipment operation in order to timely detect and handle potential problems,and ensure the stable operation of the equipment.Through this comprehensive diagnostic process,fault diagnosis of complete sets of new energy power generation equipment has been successfully achieved.The experimental results show that this method not only significantly improves the accuracy of fault diagnosis,reduces misjudgment rates,but also effectively saves the time and cost of fault diagnosis,providing strong technical support and guarantee for the continuous and stable operation of new energy generation equipment.关键词
半监督SVM模型/电力设备故障/故障诊断/新能源Key words
semi-supervised SVM model/power equipment failure/fault diagnosis/new energy分类
信息技术与安全科学引用本文复制引用
董鹏程,阎凯..基于半监督SVM模型的新能源发电成套电力设备故障诊断方法[J].机电工程技术,2025,54(10):164-167,4.
