| 引用本文: | 周振宇,俞 侃,丁 澍,姜 山,朱 珂.基于残留波形数据概率分布差异性的暂态扰动检测方法[J].电力系统保护与控制,2022,50(19):138-145.[点击复制] |
| ZHOU Zhenyu,YU Kan,DING Shu,JIANG Shan,ZHU Ke.Detecting transient disturbance with probability distribution difference of residual waveform data[J].Power System Protection and Control,2022,50(19):138-145[点击复制] |
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| 摘要: |
| 随着扰动波形数据在设备状态监测方面的应用日益广泛，一种考虑扰动特征多样性和不显著性的扰动检测算法对于提高设备初期故障诊断准确性具有实际意义。因此，从噪声而不是扰动的特征出发，提出了一种基于残留波形数据概率分布差异性的暂态扰动检测方法。首先给出了残留波形数据及其概率密度的获取方法。通过对实测波形数据中噪声的概率分布开展规律性分析，选取含有扰动与否的各周期残留波形数据间概率密度的差异性作为扰动研判的依据。然后，建立基于各周期残留波形数据概率密度间Wasserstein距离的暂态扰动检测算法及其阈值确定方法。仿真和现场数据验证结果表明，所提扰动波形检测方法对电压、电流波形采样数据中的各种不显著扰动具有较高的检测准确性。 |
| 关键词: 扰动检测 噪声 概率分布 核密度估计 初期故障 |
| DOI：DOI: 10.19783/j.cnki.pspc.211671 |
| 投稿时间：2021-12-08修订日期：2022-02-12 |
| 基金项目:国家电网有限公司总部科技项目资助“基于物联网技术的配电开关一二次深度融合与精益运维关键技术研究及应用”(52130421000S) |
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| Detecting transient disturbance with probability distribution difference of residual waveform data |
| ZHOU Zhenyu,YU Kan,DING Shu,JIANG Shan,ZHU Ke |
| (1. Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University,
Jinan 250061, China; 2. Architectural Design and Research Institute Co., Ltd., Zhejiang University,
Hangzhou 310000, China; 3. State Grid Fuyang Power Company, Fuyang 236000, China;1. Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University,
Jinan 250061, China; 2. Architectural Design and Research Institute Co., Ltd., Zhejiang University,
Hangzhou 310000, China; 4. State Grid Fg Power Company, Fuyang 236000, China)) |
| Abstract: |
| With the increasing application of disturbance waveform data in equipment condition monitoring, a disturbance detection algorithm considering the diversity and insignificance of disturbance characteristics is of practical significance in improving the accuracy of equipment initial fault diagnosis. Therefore, based on the characteristics of noise rather than disturbance, a transient disturbance detection method is proposed based on the difference in the probability distribution of residual waveform data. First, the acquisition method of residual waveform data and its probability density is given. Through the regularity analysis of the probability distribution of noise in measured waveform data, the difference of probability density between residual waveform data with or without disturbance is selected as the basis of disturbance analysis. Then the transient disturbance detection algorithm and threshold determination method based on Wasserstein distance between probability density of residual waveform data of each period are established. Simulation and field data verification results show that the proposed disturbance waveform detection method has high detection accuracy for various non-significant disturbances in voltage and current waveform sampling data.
This work is supported by the Science and Technology Project of the Headquarters of State Grid Corporation of China (No. 52130421000S). |
| Key words: disturbance detection noise probability distribution kernel density estimation incipient failure |
