| 引用本文: | 郑海龙,吕桂贤,江覃晴,等.基于信息熵理论对变压器光纤传感器声探测信号的特征提取及识别研究[J].电力系统保护与控制,2024,52(10):156-166.[点击复制] |
| ZHENG Hailong,LÜ Guixian,JIANG Tanqing,et al.Feature extraction and recognition of transformer fiber optic sensor acoustic detectionsignals based on information entropy theory[J].Power System Protection and Control,2024,52(10):156-166[点击复制] |
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| 摘要: |
| 光纤传感器以其灵敏度高、不受电磁干扰等特点得到了广泛的研究应用，但在对变压器故障声信号的采集过程中也存在噪声成分含量较高、信号特征提取不易或无法提取的缺点。为此，提出利用信息熵理论对变压器故障声信号进行特征提取分析，并基于支持向量数据描述(support vector data description, SVDD)对求取的特征量进行识别研究。在实验室搭建了变压器故障声信号实验与探测平台，采集3种典型放电模型的声信号，基于信息熵理论，选取模糊熵、能量熵、奇异谱熵和功率谱熵等对滤波后的声信号进行特征提取，形成识别特征向量。最后，利用SVDD算法对求取的特征量进行识别研究。实验结果显示，基于信息熵理论提取的故障声信号特征量识别正确率均达到90%以上，优于传统时频域特征提取和基于小波变换的特征提取方法，证明了所提出方法的可行性。 |
| 关键词: 变压器 光纤传感器 信息熵理论 特征提取 |
| DOI：10.19783/j.cnki.pspc.231410 |
| 投稿时间：2023-11-03修订日期：2024-01-26 |
| 基金项目:国家重点研发计划项目资助(2021YFB2401100)；河南省电力公司科技项目资助(521740220001) |
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| Feature extraction and recognition of transformer fiber optic sensor acoustic detectionsignals based on information entropy theory |
| ZHENG Hailong1,LÜ Guixian2,JIANG Tanqing2,ZHAO Wenbin1,WANG Bin3,CHU Penghao3,ZHAO Huiguang3 |
| (1. College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200020, China; 2. College of
Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China;
3. Xinxiang Power Supply Company, State Grid Henan Electric Power Co., Ltd., Xinxiang 453000, China) |
| Abstract: |
| Fiber optic sensors have been extensively researched and widely applied because of their high sensitivity and immunity to electromagnetic interference. However, in the process of collecting fault acoustic signals from transformers, there is a drawback of relatively high noise content, making signal feature extraction difficult or sometimes impossible. To address this issue, this paper proposes to use information entropy theory for feature extraction analysis of transformer fault acoustic signals and conduct recognition research on the extracted features based on support vector data description (SVDD). An experimental platform for collecting transformer fault acoustic signals is built in the laboratory, and signals from three typical discharge models are acquired. Based on information entropy theory, four types of entropy values, including fuzzy, energy, singular spectrum and power spectrum entropy, are selected to extract features from the filtered acoustic signals, forming recognition feature vectors. Finally, the SVDD algorithm is employed to study the recognition of the extracted features. The results demonstrate that the recognition accuracy of the extracted fault acoustic signal features exceeds 90%, surpassing traditional time-frequency domain feature extraction and wavelet transform-based feature extraction methods. This confirms the feasibility of the proposed approach. |
| Key words: transformer optical fiber sensor information entropy theory feature extraction |
