| 引用本文: | 韩宝国,马驰,李静鹏,等.基于DTCWT与LLE算法的变压器局部放电特高频信号特征参数提取方法[J].电力系统保护与控制,2019,47(20):65-72.[点击复制] |
| HAN Baoguo,MA Chi,LI Jingpeng,et al.A feature parameters extraction method of PD UHF signal based on DTCWT and LLE algorithm[J].Power System Protection and Control,2019,47(20):65-72[点击复制] |
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| 摘要: |
| 提出了一种基于对偶树复小波变换(Dual?tree Complex Wavelet Transform，DTCWT)与局部线性嵌入(Locally Linear Embedding，LLE)算法的局部放电特高频信号特征参数提取方法，可以有效识别典型变压器内部绝缘缺陷。首先采用DTCWT算法分解变压器局部放电特高频信号，得到一系列不同变化尺度下细节分量信号。再提取出各细节分量信号的偏斜度和峭度作为初始特征参数。采用LLE算法对初始特征参数组成的特征向量进行降维处理，得到最终的特征参数及特征向量，输入到支持向量机(Support Vector Machine，SVM)中识别各类绝缘缺陷。结果表明，该特征参数可以有效识别典型变压器内部绝缘缺陷，模拟绝缘缺陷识别准确率达到98.35%，现场检测信号识别准确率达到92.1%。 |
| 关键词: 变压器 局部放电 特高频 特征参数 特征提取 模式识别 |
| DOI：10.19783/j.cnki.pspc.181573 |
| 投稿时间：2018-12-19修订日期：2019-02-25 |
| 基金项目:国家电网公司科技项目资助(2018A-058) |
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| A feature parameters extraction method of PD UHF signal based on DTCWT and LLE algorithm |
| HAN Baoguo,MA Chi,LI Jingpeng,WANG Hongfu,LIU Changdao,GAO Tao |
| (Linyi Power Supply Company, State Grid Shandong Electric Power Company, Linyi 276000, China) |
| Abstract: |
| A feature parameters extraction method of Partial Discharge (PD) Ultra-High Frequency (UHF) signal based on Dual?Tree Complex Wavelet Transform (DTCWT) and Locally Linear Embedding (LLE) is proposed. The typical transformer internal insulation defects can be recognized effectively based on the characteristic parameter. At first, the PD UHF signals of transformer are decomposed by DTCWT, and a series of detail component signals at different scales of variation can be obtained. The skewers and kurtosis of every detail component signal are extracted as the original feature parameters. Through the LLE method, the eigenvector consist of original feature parameters can be dealt with dimension reduction to get the final feature parameters and eigenvector. The eigenvector are inputted into the support vector machine to pattern recognition. The results show that the propos feature parameters can be used to recognize the typical PD UHF signal in transformer, the overall recognition accuracy of simulated insulation defects reached 98.35%, and the overall recognition accuracy of field test signals reached 92.1%. This work is supported by Science and Technology Project of State Grid Corporation of China (No. 2018A-058). |
| Key words: transformer partial discharge ultra-high frequency feature parameters feature extraction pattern recognition |
