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Transformer Fault Diagnosis Method and System Based on Weighted Double Hidden Naive Bayes
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A transformer fault and diagnosis method technology, which is applied to instruments, measuring electrical variables, unstructured text data retrieval, etc., can solve problems such as long diagnosis time, diagnostic errors, and low diagnostic efficiency, and achieve improved accuracy, improved efficiency, and The effect of improving the accuracy rate and diagnosis time
Active Publication Date: 2021-08-24
QILU UNIV OF TECH
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[0005] However, the inventors found that the existing fault diagnosis methods have problems such as long diagnosis time, low diagnosis efficiency and diagnostic errors in traditional fault diagnosis.
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Embodiment 1
[0026] Embodiment 1, this embodiment provides a transformer fault diagnosis method based on weighted double hidden naive Bayesian;
[0027] Transformer fault diagnosis method based on weighted double hidden naive Bayesian, including:
[0028] S1: Obtain transformer fault data to be classified;
[0029] S2: Input the transformer fault data to be classified into the pre-trained double implicit naive Bayesian network classifier based on attribute value weighting, and output the classification result.
[0030] As one or more examples, such as figure 1 As shown, the double-hidden naive Bayesian network classifier based on attribute value weighting, in transformer fault diagnosis, C is a class node, and C represents a fault class variable set, pointing to all attribute nodes A 1 ,A 2 ,…A n , the set of symptom variables, for each attribute A i Both have two hidden parent nodes A hpi1 and A hpi2 , where i=1,2,…,n, that is to say A hpi1 and A hpi2 For each symptom variable A ...
Embodiment 2
[0114] Embodiment 2, this embodiment provides a transformer fault diagnosis system based on weighted double hidden naive Bayesian;
[0115] Transformer fault diagnosis system based on weighted double hidden naive Bayesian, including:
[0116] An acquisition module configured to: acquire transformer fault data to be classified;
[0117] The classification module is configured to: input the transformer fault data to be classified into a pre-trained double hidden naive Bayesian network classifier based on attribute value weighting, and output a classification result.
[0118] The present disclosure also provides an electronic device, including a memory, a processor, and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, each operation in the method is completed. For brevity, I won't repeat them here.
[0119] Described electronic device can be mobile terminal and non-mobile terminal, and non-mo...
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Abstract
The disclosure discloses a transformer fault diagnosis method and system based on weighted double-implicit naive Bayesian, including: obtaining transformer fault data to be classified; inputting the transformer fault data to be classified into a pre-trained double-implicit In the naive Bayesian network classifier, the classification result is output. The diagnosis method of double hidden naive Bayesian network weighted by attribute value proposed in this disclosure in transformer fault diagnosis can solve the problems of long diagnosis time, low diagnosis efficiency and diagnostic errors in traditional fault diagnosis, and through the naive Bayesian Improvement further improves the accuracy and diagnosis time of fault diagnosis, thereby improving the efficiency of diagnosis and proving the effectiveness of the algorithm.
Description
technical field [0001] The present disclosure relates to the technical field of fault diagnosis, in particular to a transformer fault diagnosis method and system based on weighted double hidden naive Bayesian. Background technique [0002] The statements in this section merely mention background art related to the present disclosure and do not necessarily constitute prior art. [0003] In the process of realizing the present disclosure, the inventors found that the following technical problems existed in the prior art: [0004] With the rapid development of industrial productivity and the continuous updating of equipment, system failures have become more complicated and diverse. In the power system, the failure of the transformer not only causes the power outage to stop the operation of the equipment, but also seriously hinders the development of the economy. There are many methods of fault diagnosis, especially with the emergence of artificial intelligence in recent years...
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