Method for monitoring and evaluating actual state of MOV based on outlier detection

Abstract

The invention discloses a method for monitoring and evaluating the actual state of an MOV based on outlier detection. The method comprises the steps of: carrying out the sampling of various actual parameters at different time points at the moment of the operation of the MOV for several times, and forming an original data set; performing principal component analysis on the original data set to reduce the dimension of the collected features, and achieving visualization on the two-dimensional or three-dimensional level; then, calculating a distance between two adjacent points in the data set after PCA, a kth distance, a kth distance neighborhood, a reachable distance and a local reachable density according to a local abnormal factor; and finally, calculating a local outlier factor to obtain an outlier detection evaluation result of any sampling data of the MOV. According to the invention, the executable difficulty of traditional online monitoring is reduced, the method for monitoring andevaluating the actual state of the MOV based on outlier detection has the higher industrial application value, enhances the effective way of monitoring and maintaining the lightning protection deviceby people, and avoids the risk conditions of damage, fire, explosion and the like of an electrical system caused by abnormal operation or degradation of an MOV device.

Description

technical field [0001] The invention belongs to the field of monitoring design of lightning protection devices, in particular to a method for monitoring and evaluating the actual state of MOV based on outlier detection. Background technique [0002] Metal oxide varistor (MOV) is a resistive device with nonlinear volt-ampere characteristics. When there is a risk of lightning overvoltage, it uses voltage clamping to protect sensitive devices in the system. Therefore, it is widely used in lightning surge protection of various electrical and communication systems. In addition to threats such as lightning overvoltage and operating overvoltage during operation, MOVs are also affected by external environmental factors such as temperature and humidity, chemical pollution, and pollution, resulting in changes in electrical characteristics and physical states. This will lead to abnormal volt-ampere characteristics of the MOV, a decrease in thermal stability, and even thermal collapse ...

AI Technical Summary

Problems solved by technology

In recent years, the combination of data and algorithms has become a new research method. Yang Zhongjiang et al. have proposed MOV online monitoring based on genetic algorithms, and can monitor the performance during the period by optimizing the parameters reflecting device degradation in the equivalent model. , provides a new direction for the follow-up online monitoring technology research, but the algorithm itself has many parameters, the selection of these parameters seriously affects the quality of the final solution, and most of the selection of these parameters is based on experience at present.

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