A Method of OLTC Fault Diagnosis Based on Fuzzy Clustering

Abstract

The invention discloses a method for OLTC fault diagnosis based on fuzzy clustering, comprising the steps of (1) attaching a vibration detection probe to the top of the box wall of an on-load tap changer, respectively collecting the normal state of the on-load tap changer, contact The vibration signals generated during the action process in the loose state, the contact wear state, and the contact burnt state, and the vibration signals in each state are collected in multiple groups; (2) use the wavelet packet threshold method to reduce the noise of each vibration signal; ( 3) Extract the feature quantity from the noise-reduced vibration signal; (4) Use fuzzy clustering to identify faults. The method can monitor the working state of the transformer on-load tap-changer in real time, and meets the requirement of real-time fault diagnosis of the transformer on-load tap-changer. Provide data support and theoretical basis for purposeful maintenance, and avoid wasting manpower, material resources and time.

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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