Thermal Infrared Image Recognition Method of Power Module Fault of VSC-HVDC Converter Valve Based on Temperature Probability Density

SHENG Yujun; SUN Jie; LI Guoqiang; XIE Xiaodong; ZHOU Kai; ZHANG Xiaodi

doi:10.13296/j.1001-1609.hva.2026.02.025

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Thermal Infrared Image Recognition Method of Power Module Fault of VSC-HVDC Converter Valve Based on Temperature Probability Density

Research & Analysis | 更新时间：2026-03-16

- Thermal Infrared Image Recognition Method of Power Module Fault of VSC-HVDC Converter Valve Based on Temperature Probability Density
- High Voltage Apparatus Vol. 62, Issue 2, Pages: 207-213(2026)
- 作者机构：
  
  国网北京市电力公司检修分公司，北京 100073
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2026.02.025
  CLC：
- Received：18 July 2025，
  
  Revised：2025-10-15，
  
  Published：16 February 2026
- 稿件说明：
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SHENG Yujun, SUN Jie, LI Guoqiang, et al. Thermal Infrared Image Recognition Method of Power Module Fault of VSC-HVDC Converter Valve Based on Temperature Probability Density[J]. High Voltage Apparatus, 2026, 62(2): 207-213.
DOI：

SHENG Yujun, SUN Jie, LI Guoqiang, et al. Thermal Infrared Image Recognition Method of Power Module Fault of VSC-HVDC Converter Valve Based on Temperature Probability Density[J]. High Voltage Apparatus, 2026, 62(2): 207-213. DOI： 10.13296/j.1001-1609.hva.2026.02.025.

摘要

为了准确识别柔直换流阀功率模块故障，保证高压直流输电安全、稳定地运转，提出基于温度概率密度的柔直换流阀功率模块故障热红外图像识别方法。通过热红外图像中目标约束区域和背景采样区域的核密度估计，提取柔直换流阀功率模块热红外图像目标区域；基于核密度估计提取温度概率密度，将其作为区分热红外图像目标区域中正常运行区域和故障区域的特征，结合热红外图像中目标区域内阈值，初步划分故障区域，将初步划分结果作为K-means聚类算法的先验知识，通过不断聚类故障区域簇类，达到识别故障的目的。实验验证：所提方法通过集成灰度、空间关系和局部标准差的核密度估计可更精准提取热红外图像目标，准确地识别到柔直换流阀功率模块的故障位置。

Abstract

For accurately identifying the fault of power module of VSC-HVDC converter valve and ensuring safe and stable operation of HVDC transmission

a thermal infrared image recognition method of power module of VSC-HVDC converter valve based on the temperature probability density is proposed. The target region of the thermal infrared image of the power module of theVSC-HVDCconverter valve is extracted by estimating the kernel density of the target constraint region and the background sampling region in the thermal infrared image.The temperature probability density is extracted based on the kernel density estimation and is used as the feature to distinguish the normal operation area and the fault area in the target area of the thermal infrared image. The fault area is preliminarily divided according to the threshold value in the target area of the thermal infrared image

and the preliminary division result is used as the prior knowledge of the K-means clustering algorithm. The purpose of fault identification is achieved bycontinuously clustering the fault area clusters. Experimental results show that the proposed method can accurately extract thermal infrared image targets by integrating the kernel density estimation of gray scale

spatial relationship and local standard deviation

and can accurately identify the fault location of the power module of theVSC-HVDCconverter valve.

关键词

Keywords

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