Abstract
The DC-bias monitoring device of a transformer is easily affected by external noise interference, equipment aging, and communication failure, which makes it difficult to guarantee the validity of monitoring data and causes great problems for future data analysis. For this reason, this paper proposes a validity evaluation method based on data driving for the on-line monitoring data of a transformer under DC-bias. First, the variation rule and threshold range of monitoring data for neutral point DC, vibration, and noise of the transformer under different working conditions are obtained through statistical analysis. Then, the data validity criterion of DC bias monitoring data is proposed to achieve a comprehensive evaluation of data validity based on data threshold, continuity, impact, and correlation. In addition, case studies are carried out on the real measured data of the DC bias magnetic monitoring system of a regional power grid by using this evaluation method. The results show that the proposed method can systematically and comprehensively evaluate the validity of the DC bias monitoring data and can judge whether the monitoring device fails to a certain extent.
Highlights
The main transformer of the urban power grid is affected by the stray current of the subway, which produces the phenomenon of DC bias [1,2,3]
According to the statistical rules of DC bias monitoring data obtained by many tests and the general requirements for the DC bias parameters of the transformer, the parameters of data validity evaluation Criterion 2–5 were set
This paper proposes a validity evaluation method based on data driving for on-line monitoring
Summary
The main transformer of the urban power grid is affected by the stray current of the subway, which produces the phenomenon of DC bias [1,2,3]. On-line monitoring devices for DC bias have been installed in many urban power grids in China, such as Shanghai, Guangzhou, Guiyang, and so on [7,8]. The installation of the DC bias monitoring device realizes the on-line monitoring of neutral point DC, vibration, and noise of the transformer [9,10]. In the practical application, the DC bias monitoring sensors are affected by external noise interference, equipment aging, and communication failure [11,12,13]. To solve the above problems, it is of great engineering application value to study the validity evaluation method for on-line monitoring data of the transformer under DC-bias
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