Abstract
In the process of online real-time monitoring of intelligent digital filter high-voltage switchgear, the mechanical state diagnosis of high-voltage circuit breaker is based on fully understanding the mechanical characteristics of each component of circuit breaker. In this paper, K-means clustering algorithm is applied to the mechanical state detection of digital filter high-voltage switchgear. The mechanical state of the digital filter high-voltage switchgear is monitored in real time by using 126 kV GIS. Through fault simulation experiment and mechanical stability experiment, the corresponding changes of characteristics of each mechanical component of circuit breaker in signal waveform and corresponding data changes are found. The experimental results show that this method has good running speed and stability and is more suitable for the real-time monitoring of intelligent digital filter high-voltage switchgear.
Highlights
With the continuous development of science and technology and the continuous improvement of economic level, the requirements for the safety and stable operation of the system have been gradually improved [1,2,3]
The system instability has caused safety events of different severity, which has a greater impact on product production and product quality. e fault of the digital filter high-voltage switch machinery usually occurs through abnormal vibration and automatically detects and recognizes the system according to the vibration signal. e continuous development of the technology improves the equipment maintenance mode
When the device exception, the data collected varies substantially from the usual data. erefore, in the mechanical state real-time monitoring system of the intelligent digital filtering highvoltage switching device, the number of clusters is by default to 2 when the K-means cluster algorithm is used for clustering normally. e equipment has only two normal and abnormal states
Summary
With the continuous development of science and technology and the continuous improvement of economic level, the requirements for the safety and stable operation of the system have been gradually improved [1,2,3]. The industrial field is gradually realizing automation, highspeed, and continuous development. What is urgently solved in various industries and fields is to use computers to monitor the digital-filtered high-voltage switch mechanical status of system faults in real time. E fault of the digital filter high-voltage switch machinery usually occurs through abnormal vibration and automatically detects and recognizes the system according to the vibration signal. There are many outfield disturbances (nonlinearity, modeling errors, parameter perturbations, and many other uncertainties) in practice, while the fault detection filter does not consider robustness. The K-means clustering algorithm is applied to the effective monitoring of the digital filtering high-pressure open mechanical state and constructs the data model. This paper takes 126 kV intelligent GIS as the experimental research goal to verify the effectiveness and practicability of the proposed method
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