| 引用本文: | 陈嘉宁,杨翾,叶承晋,等.基于缺失数据修复的变压器在线故障诊断方法[J].电力系统保护与控制,2019,47(15):86-92.[点击复制] |
| CHEN Jianing,YANG Xuan,YE Chengjin,et al.On-line fault diagnosis method for power transformer based on missing data repair[J].Power System Protection and Control,2019,47(15):86-92[点击复制] |
|
| 摘要: |
| 数据质量是影响变压器故障诊断正确率的重要因素。为了解决变压器油色谱在线监测数据缺失问题，提出了一种基于缺失数据修复的变压器在线故障诊断方法，利用改进k-最邻近和多分类SVM的循环迭代实现基于缺失数据的变压器故障诊断。在k-最邻近方法中，提出以相关系数的负指数为权值的曼哈顿距离来度量样本间距离。一方面用以突出强相关指标对缺失信息的影响，提高数据修复的准确性。另一方面改进的曼哈顿距离适用于基于k-d树的高效搜索策略，可以实现针对海量历史数据的快速搜索，满足在线诊断对算法实时性的需求。实例诊断的结果表明，该方法可以有效降低数据缺失对变压器故障诊断正确率的影响，有利于实现变压器故障的准确、高效在线诊断。 |
| 关键词: 变压器 故障诊断 k-最邻近 k-d树 支持向量机 |
| DOI：10.7667/PSPC20191512 |
| 投稿时间：2018-08-11修订日期：2018-09-14 |
| 基金项目:国家自然科学基金项目资助(51807173);国网浙江省电力有限公司群众创新项目(5211HZ18004L) |
|
| On-line fault diagnosis method for power transformer based on missing data repair |
| CHEN Jianing,YANG Xuan,YE Chengjin,TANG Jian,LI Xiang,FANG Xiang,LONG Houyin |
| (Hangzhou Power Supply Company of State Grid Electric Power Corporation Ltd., Hangzhou 310009, China;College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China) |
| Abstract: |
| Data quality is an important factor affecting the accuracy of transformer fault diagnosis. In order to solve the problem of missing on-line monitoring data for transformer dissolved gas, an on-line fault diagnosis method using loop iterations of improved k-nearest neighbors and multi-class SVMs for power transformer based on missing data repair is proposed. In the k-nearest neighbor method, the Manhattan distance which is weighted by the negative exponent of the correlation coefficient is used to measure the distance between samples. On one hand, the influence of the strong correlation indexes on the missing data can be highlighted to improve the accuracy of data repair. On the other hand, the improved Manhattan distance is suitable for an efficient search strategy based on k-d tree, which can achieve fast search for massive historical data and meet the real-time demand of on-line diagnosis. Diagnosis test results show that the proposed method can reduce the influence of missing data on the accuracy of transformer fault diagnosis and realize the accurate and efficient on-line diagnosis for transformer fault. This work is supported by National Natural Science Foundation of China (No. 51807173) and Mass Innovation Project of State Grid Zhejiang Electric Power Company Ltd. (No. 5211HZ18004L). |
| Key words: transformer fault diagnosis k-nearest neighbor k-dimensional tree support vector machine |
