OLTC fault diagnosis method based on fuzzy clustering

Abstract

The invention discloses an OLTC fault diagnosis method based on fuzzy clustering. The method comprises the steps of (1) attaching a vibration detection probe to the top end of a box wall of an on-loadtap changer, and collecting multiple groups of vibration signals generated in an action process by the on-load tap changer in each of a normal state, a contact loose state, a contact wear state and acontact burnout state; (2) denoising the vibration signals by using a wavelet packet threshold method; (3) extracting characteristic quantities for the denoised vibration signals; and (4) performingfault identification by the fuzzy clustering. According to the method, a working state of the on-load tap changer of a transformer can be monitored in real time, and the requirement for real-time fault diagnosis of the on-load tap changer of the transformer is met. A data support and a theoretical basis are provided for purposeful maintenance, so that the waste of manpower, material resources andtime is avoided.

Description

technical field [0001] The invention relates to a method for diagnosing faults of electric equipment, in particular to a method for diagnosing OLTC faults based on fuzzy clustering. 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 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 transformer itself, and then cause other...

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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