一种通用电能质量波形异常的自适应检测方法

Xiaomei Yang, Chaoyun Guo, Yuewan Luo
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引用次数: 4

摘要

电能质量监测数据广泛应用于设备故障检测与诊断,如电缆故障检测。成功地从电能质量数据中检测出异常信号尤为重要。提出了一种基于残差奇异值(RSV)的通用异常检测方法。该方法利用电能质量波形的本质特征,对残差信号构成的矩阵进行奇异值分解(SVD)。异常可以通过测量奇异值的变化来检测。此外,挖掘了噪声和奇异值的特征,提出了自适应阈值进行异常检测。仿真数据和现场实测数据验证了该算法的可行性和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel adaptive detection method for generic power quality waveform abnormality
The power quality monitoring data are widely applied in equipment fault detection and diagnosis, such as cable fault detection. Successfully detecting the abnormal signal from the power quality data is particularly important. This paper pro posed a generic abnormality detection method based on the residual-singular-value (RSV). The proposed method takes advantage of the essential characteristics of power quality waveform and the singular value decomposition (SVD) is performed on a matrix constructed by residual signals. Abnormality can be detected by measuring the change of singular values. In addition, the features of noise and singular value is excavated, then the adaptive threshold is proposed for abnormality detection. Simulation data and the field-measured data are both used to verify the feasibility and accuracy of the proposed algorithm.
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