Abstract
Nowadays, power electronic technology is widely affecting people’s daily work and life. However, there are still many problems in the current power supply research. When the fault information of power transformer is not complete or there is some ambiguity or even the information is lost, it will largely lead to the conclusion and correct conclusion of fault diagnosis. In this case, the fuzzy theory is applied to the fault diagnosis of shunt capacitor, and the fuzzy fault diagnosis system of shunt capacitor is studied. At the same time, a map-based fault diagnosis system is proposed. In this paper, the cloud computing technology is introduced into the deep learning and compared with SVM and DBN algorithm. The research results of this paper show that the accuracy of fuzzy diagnosis results is 94%, 84%, 90%, 80%, 83%, and 70%, respectively, which shows that the model diagnosis reliability is relatively high. Among the three algorithms, MR-DBN overall detection rate is higher and the time-consuming is lower than the other two methods. The diagnostic accuracy and misjudgment rate of DBN are as follows: 96.33% and 3.90%. The diagnosis accuracy and misjudgment rate of SVM are as follows: 96.40% and 3.83%. The diagnostic accuracy and misjudgment rate of MR-DBN are, respectively, 99.52% and 0.57%. Compared with the other two methods, MR-DBN has the highest diagnostic accuracy and the lowest error rate, which to a large extent indicates that MR-DBN algorithm has higher diagnostic accuracy and has greater advantages and reliability in power supply diagnosis and identification. It not only improves the accuracy of power capacitor fault diagnosis and identification but also provides a new method for the application of power capacitor fault research and development.
Highlights
With the rapid development of science and technology, fault diagnosis has been paid more and more attention
Chen et al proposed a correlation model for fault diagnosis and prediction of thermal power units based on deep learning and multimedia system, which greatly improved the balance between power systems. e research results of Chen et al show that this method is reliable
When the accuracy of diagnosis results is greater than 90%, it means that the reliability of model diagnosis is very high
Summary
With the rapid development of science and technology, fault diagnosis has been paid more and more attention. E voltage level in the power system can reduce the related power loss and improve the related state performance of the power system; the normal operation of the power capacitor is related to the stability and economy of the power system operation. E method of power capacitor fault diagnosis and identification based on degree is very important. The dielectric loss factor identification algorithm has accuracy, it still lacks a certain economy [1]. Chen et al proposed a correlation model for fault diagnosis and prediction of thermal power units based on deep learning and multimedia system, which greatly improved the balance between power systems. The stack automatic coding network has accuracy, it lacks certain stability [3]
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