Probability forecasting method and system of switch equipment faults
A technology for faults and switching equipment, applied in circuit breaker testing, instruments, calculations, etc., can solve problems such as limited application and promotion, lack of gas decomposition products and equipment failure models or criteria, and data processing of test results
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Embodiment 1
[0098] This embodiment is a method for predicting the probability of partial discharge in switchgear, for partial discharge, with adsorbent partial discharge and without adsorbent partial discharge SF 6 The gas decomposition product detection data is processed for data processing, and then the probability of equipment failure is predicted.
[0099] Select the SF detected by the partial discharge fault 6 The two gases with a large content of gas decomposition products are SOF 2 and S 2 OF 10 , a total of 60 sets of data, the calculated characteristic parameter is SOF 2 with S 2 OF 10 content ratio. SOF generated by Weibull distribution (1), normal distribution (3) and lognormal distribution (5) for three kinds of partial discharge faults 2 and S 2 OF 10 Data statistics, parameter estimation and error analysis are carried out on the detection data, and the distribution parameters (γ and θ, μ and σ) and correlation χ under different fault forms are calculated 2 , listed...
Embodiment 2
[0109] This embodiment is a method for predicting the probability of abnormal heating of switchgear, which is basically the same as the method and principle of Embodiment 1, except that:
[0110] In this example, SF with abnormal heat generation, abnormal heat generation with adsorbent and abnormal heat generation without adsorbent 6 The gas decomposition product detection data is processed for data processing, and then the probability of equipment failure is predicted.
[0111] Select the SF detected by the abnormal heating fault 6 The two gases with a large content of gas decomposition products are SO 2 and H 2 S, a total of 80 sets of data, the calculated characteristic parameter is SO 2 with H 2 S content ratio. SO produced by Weibull distribution (1), normal distribution (3) and lognormal distribution (5) for three kinds of abnormal heating faults 2 and H 2 Data statistics, parameter estimation and error analysis are carried out on the S detection data, and the dis...
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