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首页> 外文期刊>Science, Measurement & Technology, IET >HANKEL-EM-SVD: a hybrid data dropout estimation technique for high voltage partial discharge signals
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HANKEL-EM-SVD: a hybrid data dropout estimation technique for high voltage partial discharge signals

机译：HANKEL-EM-SVD：用于高压局部放电信号的混合数据丢失估计技术

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摘要

Partial discharge (PD) measurement and characterisation is the most effective method to assess the insulation conditions of equipment for its diagnosis. A significant challenge in this area is PD denoising. Despite recent advancements in denoising algorithms, data drops out because of inappropriate threshold selection during denoising. Processing PD signals with missing data leads to an inaccurate assessment of the insulation conditions, indicating recovering such missed data is a potential research scope under the moonlight. This study addresses the issues of recovering such data dropouts by considering the PD data vector matrix as a low-rank matrix that needs completion by Hankel-matrix decomposition methods. After finding the probabilistic estimate of the missing values using an expectation maximisation algorithm, singular-value decomposition is used to find the actual missing values by soft-thresholding the singular values of the matrix. After testing this technique on simulated PD data, it is implemented to test SASTRA-High-Voltage Laboratory data, showing root mean square error (RMSE) 0.77% and mean absolute error 0.84%. The efficiency of this technique is confirmed when tested with large-sized noise free PD data of 36 samples from the Laboratory of BOLOGNA University (with RMSE varying from 0.15 to 0.31% and mean absolute error varying from 0.29 to 0.6%).
机译：局部放电（PD）的测量和表征是评估设备绝缘状况以进行诊断的最有效方法。该领域的一个重大挑战是PD去噪。尽管最近在降噪算法方面取得了进步，但是由于在降噪过程中阈值选择不当，导致数据丢失。用丢失的数据处理PD信号会导致对绝缘条件的评估不准确，这表明在月光下恢复此类丢失的数据是一个潜在的研究范围。这项研究通过将PD数据向量矩阵视为需要通过Hankel-matrix分解方法完成的低秩矩阵来解决恢复此类数据丢失的问题。在使用期望最大化算法找到缺失值的概率估计之后，使用奇异值分解通过对阈值进行软阈值化来找到实际缺失值。在模拟的PD数据上测试此技术后，将其实施以测试SASTRA-高压实验室数据，显示均方根误差（RMSE）0.77％和平均绝对误差0.84％。当使用来自博洛尼亚大学实验室的36个样本的大尺寸无噪声PD数据进行测试时，该技术的效率得到了证实（RMSE的变化范围为0.15至0.31％，平均绝对误差范围为0.29至0.6％）。

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