Current situation and prospect of fault diagnosis technology for mine dry-type transformer
The feature extraction methods for fault diagnosis signals include frequency response method,wavelet transform method,and stacked auto encoder;Fault diagnosis methods include Bayesian networks,support vector machines,and BP neural networks;State assessment and life prediction methods include cross entropy combination prediction method,grey theory,and analytic hierarchy process.To improve the safety of dry-type transformers during underground operation and reduce losses caused by faults.The research status of fault diagnosis signal feature extraction method,fault diagnosis method,state evaluation and life prediction method,were introduced and compared.Finally,an outlook was given on the fault diagnosis technology of dry-type transformers,pointing out that the fusion of fault diagnosis feature quantities,the application of big data and artificial intelligence can provide a good solution for future research on dry-type transformer fault diagnosis.
dry-type transformers for miningfault diagnosisfeature extractioncondition evaluationlifetime prediction
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