基于泄漏电流时频奇异谱和模糊聚类的触电故障诊断

针对实测触电故障信号具有非平稳特性而不易被辨识问题,提出了一种基于泄漏电流时频奇异谱和模糊聚类的触电故障诊断方法。首先,利用平滑伪威格纳-维尔分布(smoothed pseudo wigner-ville distribution,SPWVD)对触电故障信号进行时频分析并依据信号的能量分布特征选择时频区域;然后对选择的时频区域进行奇异谱分析,以获取的局部时频矩阵奇异值作为触电信号的特征量输入FCM,即可实现触电信号的故障诊断。对剩余电流保护装置试验平台上获取的实测触电故障信号的时频矩阵奇异值进行模糊C均值聚类,结果表明该方法识别准确率为97.50%,平均识别时间为0.008 5 s,其中植物和动物触电测试样本识别准确率分别为100%,95.00%,从而验证了基于泄漏电流时频奇异谱和模糊聚类的触电故障诊断方法的有效性,该研究可为研发新一代基于触电故障诊断的剩余电流保护装置提供理论依据和方法参考。

Diagnosis of electric shock fault based on time-frequency singular value spectrum of leakage current and fuzzy clustering

Abstract: Residual current devices (RCDs), a type of protective equipment in low-voltage systems, are widely used to prevent and avoid leakage accident of power grid and protect the safety of life and property. At present, the operation of an RCD is based on sensing the root mean square value of residual current in an electrical circuit. The circuit will be interrupted on the action of a relay when the residual current exceeds a predetermined level (30 mA for human safety), known as the tripping current. Although such devices offer a large degree of protection, they are prone to nuisance tripping or maloperation in the actual operation of the grid due to the lack of the ability to diagnose electric shock type and identify touch current, which reduces the reliability and the rate of proper commissioning for RCDs. Thus, aiming at the problem that the measured electric shock signals are non-stationary and difficult to diagnose the type of electric shock, a new method of fault diagnosis of electric shock signal based on time-frequency singular spectrum of leakage current and fuzzy clustering is proposed. First of all, a simulation signal is used to compare and analyze the time-frequency analysis performance of short-time Fourier transformation (STFT), wigner-ville distribution (WVD) and smoothed pseudo Wigner-Ville distribution (SPWVD). The simulation results show that the STFT presents a lower time-frequency resolution because of the fixed window function, the WVD has serious crosstalk terms and it is difficult to determine the frequency components of the signal, and the SPWVD suppresses the crosstalk of WVD and reflects the distribution of signal frequency components with time through the smoothing of time-frequency window function. Therefore, SPWVD is chosen as the time-frequency analysis method in this paper. Then, numerous groups of total leakage current signals were measured using a recorder on the electric shock experiment platform of RCDs. We select a total of 0.04 s of data (one cycle before the electric shock and one cycle after the electric shock) as electric shock sample data. The SPWVD is used to analyze the total leakage current signal to obtain the time-frequency matrix, and the frequency band width of the main spectrum energy is 0-150 Hz, which can be divided into 13 sub-bands. The singular value decomposition (SVD) is adopted to decompose the time-frequency matrix formed by 13 sub-bands, and the singular values corresponding to the respective sub-frequency band are obtained as the feature vectors of the electric shock signal. And then the fuzzy C means (FCM) algorithm is applied to perform the clustering of extracted feature vectors to get the electric shock signal type. Finally, a total of 400 groups of animals and plants shock data are used as the research object. Among them, 140 groups of animal electric shock samples and 140 groups of plant electric shock samples are taken as known samples, and 60 groups of animal electric shock samples and 60 groups of plant electric shock samples are used as test samples. The experimental results show that there are 3 groups of samples in 120 groups of test samples which are wrongly identified and the recognition accuracy rate is 97.50%. Among them, the accuracy rate of plant electric shock test sample is 100%, and there are 3 samples in animal electric shock test samples, which are identified incorrectly and the recognition accuracy rate is 95.00%. The above results verify the correctness and validity of diagnosing the type of the electric shock fault signal by the extracted characteristic value of the total leakage current, which can lay a solid theoretical and technical foundation for developing new generations of adaptive residual current protection devices.