OLTC fault diagnosis method based on combination of wavelet packet and neural network

Abstract

The invention discloses an OLTC fault diagnosis method based on combination of a wavelet packet and a neural network. The method comprises the following steps: (1) applying a vibration detection probeon a top end of a tank wall of an on-load tap-changer, respectively vibration signals produced in the action process under a normal state, a contact loose state, a contact abrasion state and a contact burning state of the on-load tap-changer, and respectively collecting multiple groups of vibration signals under each state; 2) decomposing the OLTC vibration signals into different frequency bandsutilizing a wavelet decomposition principle; (3) computing an energy spectrum entropy of each frequency band, thereby constructing a wavelet packet energy spectrum entropy vector as the input vector of the neural network; and (4) performing fault mode recognition by utilizing the method of combining the confidence coefficient and the neural network. A working state of a transformer on-load tap-changer can be monitored in real time, a real-time fault diagnosis requirement of the on-load tap-changer can be satisfied, the data support and theoretical evidence are provided for the purposeful overhauling, and the manpower, the material resource and time waste are avoided.

Description

technical field [0001] The invention relates to a fault diagnosis method for electric equipment, in particular to an OLTC fault diagnosis method based on the combination of wavelet packets and neural networks. 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 of transformers, and its failure not only directly affects the operation of the transformer, but also affects the quality and operation of the power grid. According to statistics in the ditch, the 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 transform...

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

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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