Abstract
The on-board power supply system provides power for the launch vehicle. The power transmission and transformation system plays an irreplaceable role to ensure that the on-board power supply system receives the normal working voltage of the launch vehicle. There are many types of faults in power transmission and transformation systems. The traditional faulty diagnosis method of power transmission and transformation equipment has the disadvantages of being susceptible to experts’ subjectivity and model’s ossification. In this paper, a new method of equipment fault diagnosis based on big data is proposed. On the basis of big data, this paper introduces the failure mode clustering algorithm, the state parameter correlation analysis algorithm, the fault diagnosis method based on the correlation matrix, and other key fault diagnosis technologies. The fault record data of the 400 kV voltage grade oil-immersed transformer bushing in the past ten years by a Chinese combat unit is used as a case for demonstration. The results show that the accuracy rate of SC-LSTM-K-means clustering model exceeds 95%. And the fault classification mode can be accurately obtained. A priori correlation algorithm with TA coefficient can be used to evaluate the strong and weak relationship between the state parameters; the fault diagnosis matrix based on Pearson’s correlation coefficient can accurately determine the fault mode consistent with the actual operation and maintenance test results. Therefore, the fault diagnosis method of power transmission and transformation system based on big data can both effectively obtain the inherent laws of historical data and realize more accurate fault diagnosis with data adaptability.
Highlights
E on-board power supply system provides power for the launch vehicle. e power transmission and transformation system plays an irreplaceable role to ensure that the on-board power supply system receives the normal working voltage of the launch vehicle. ere are many types of faults in power transmission and transformation systems. e traditional faulty diagnosis method of power transmission and transformation equipment has the disadvantages of being susceptible to experts’ subjectivity and model’s ossification
On the basis of big data, this paper introduces the failure mode clustering algorithm, the state parameter correlation analysis algorithm, the fault diagnosis method based on the correlation matrix, and other key fault diagnosis technologies. e fault record data of the 400 kV voltage grade oil-immersed transformer bushing in the past ten years by a Chinese combat unit is used as a case for demonstration
The silhouette coefficient (SC)-long short-term memory (LSTM)-K-means clustering algorithm can be used to mine the fault modes of power transmission and transformation systems of launch vehicles, and the number of fault classifications can be determined through the SC
Summary
K-means algorithm is proposed by Ding and He [14], which can divide the data into k clusters that minimize the sum of squares of errors through continuous iterative calculation. E following equation shows the correlation coefficient of Br and Vt applied to equipment fault diagnosis: Rrt Br, Vt C o v Br, V t ,. Fp , where U is the data vector of the fault case to be diagnosed, including the state parameter level of each state parameter, and F is a fault mode diagnosis result vector, and the value of each element in the vector can indicate the membership degree of the fault case under each fault mode. When the most likely failure mode is diagnosed, the failure mode with the largest membership degree (the largest value) can be selected as the final result
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