| 基于SVDD和改进K-Means的变压器故障诊断模型 |
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| 引用本文：谢旭钦，刘泉辉，赵湘文，张清松，林剑雄，张帆.基于SVDD和改进K-Means的变压器故障诊断模型[J].计算技术与自动化,2024,(2):30-34 |
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| 中文摘要:变压器状态对于智能配电房的安全稳定运行具有重要意义。为实现对变压器故障的准确诊断，在变压器油中溶解气体分析(DGA)的基础上，提出了一种联合使用支持向量数据描述(SVDD)和改进K-Means聚类的变压器故障诊断方法。首先利用SVDD构造闭合分类曲面实现“正常”和“故障”两类判断，然后对“故障”类样本进行K-Means聚类分析，自动将其划分为低能放电、中低温过热、高能放电、高温过热和局部放电5种故障类型，同时针对K-Means初始聚类中心选取难题，提出局部密度概念自动确定K-Means初始聚类中心，提升聚类性能。最后利用变压器故障真实数据开展实验，结果表明，相较于支持向量机（SVM)和BP神经网络模型，所提方法的故障诊断准确率分别提升9.8%和8%。 |
| 中文关键词:智能配电房 变压器故障诊断 油中溶解气体分析 支持向量数据描述 多分类器联合 |
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| Transformer Fault Diagnosis Model Based on SVDD and Improved K-Means |
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| Abstract:The operation status of transformer is of great significance to the stability and reliability of intelligent distribution room. In order to realize the accurate diagnosis of transformer faults, based on the analysis of dissolved gases in transformer oil, a multi-classifier joint fault diagnosis method based on the combined use of support vector data description (SVDD) and improved K-Means clustering is proposed. First, SVDD is used to construct a closed classification surface to realize “normal” and “fault” judgments. Then K-Means clustering analysis is carried out on the “fault” samples, which are automatically divided into five types: low energy discharge, medium and low temperature overheat, high energy discharge, high temperature overheat and partial discharge. At the same time, the concept of local density is proposed to automatically determine the initial clustering center of K-Means to improve the clustering performance. Finally, the transformer fault data of the intelligent distribution room is used to carry out the verification experiment. The results show that compared with the traditional support vector machine (SVM) and BP neural network model, the fault diagnosis accuracy of the proposed method is improved by 9.8% and 8%,respectively. |
| keywords:intelligent distribution room transformer fault diagnosis analysis of dissolved gas in oil support vector data description multi-classifier association |
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