OLTC mechanical fault diagnosis method based on sample entropy and SVM

Abstract

The invention discloses an OLTC mechanical fault diagnosis method based on sample entropy and SVM. The method comprises the steps that step 1, an acceleration vibration sensor is placed on an OLTC topcover to collect vibration signals in various states; step 2, performing EEMD decomposition on the original vibration signal to obtain components IMF, and further processing the first four IMF components; step 3, calculating and selecting a sample entropy of the IMF component; step 4, for the training data set, using the sample entropy obtained through calculation as a feature vector, inputting the feature vector into an SVM for training to obtain an SVM classifier, inputting the SampEn value of the IMF component of the test sample into the SVM classifier, and outputting the SampEn value of the IMF component of the test sample through the SVM classifier to obtain the operation state of the test sample. The working state of the transformer on-load tap-changer can be monitored in real time,the real-time fault diagnosis requirement of the transformer on-load tap-changer is met, data support and theoretical basis are provided for targeted maintenance, and waste of manpower, material resources and time is avoided.

Description

technical field [0001] The invention relates to an OLTC mechanical fault diagnosis method based on sample entropy and SVM, and belongs to the technical field of electric equipment signal monitoring. Background technique [0002] The on-load tap changer (OLTC) is an important part of the power transformer, and its operating status is directly related to the stability and safety of the transformer and the system. OLTC is one of the components with the highest failure rate in transformers. Its failure not only directly affects the operation of the transformer, but also affects the quality and operation of the power grid. According to domestic statistics, accidents caused by OLTC faults account for about 28% of the total transformer accidents, and the fault types are basically mechanical faults, such as loose contacts, falling off contacts, mechanism jamming, slipping, and refusal to move. Mechanical failure will directly damage the OLTC and the transformer itself, and then ca...

AI Technical Summary

Problems solved by technology

The power outage maintenance period of on-load tap-changers is often long, and it is difficult to detect early mechanical failures in time. Failures and damages often occur before power outage maintenance, and power outage maintenance affects the normal operation of the transformer, which requires a lot of manpower, material and financial resources

[0004] On-line monitoring methods mainly include thermal noise-based diagnosis method and vibration-based on-line monitoring, etc. The thermal noise-based diagnosis is that the thermal noise generated by the heat generated after the transformer tap-changer breaks down spreads to the outside of the transformer, and is detected by installing a noise sensor on the transformer shell. To diagnose the fault of the tap changer, but when the thermal noise is transmitted to the sensor, the energy loss is too large, and various noises interfere with the engineering application, which is difficult to realize

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