Transformer winding state recognition method based on fuzzy adaptive resonance neural network

Abstract

The invention discloses a transformer winding state recognition method based on a fuzzy adaptive resonance neural network. The transformer winding state recognition method comprises the steps of (1) arranging monitoring points of a vibration sensor, setting the sampling frequency and the sampling time of a data acquisition instrument, and acquiring transformer vibration signals; (2) performing wavelet packet decomposition and reconstruction on the transformer vibration signals; (3) extracting vibration signal sub-band energy values; (4) analyzing the vibration signal sub-band energy values of each vibration monitoring point, and selecting feature band energy of valid monitoring points to construct a feature vector so as to act as input of the fuzzy adaptive resonance neural network; (5) building the fuzzy adaptive resonance neural network, adjusting network parameters until set precision is reached; and (6) performing recognition on transformer winding state through the fuzzy adaptive resonance neural network. The transformer winding state recognition method can judge a transformer winding pressing state accurately and quickly, and can be applied to online monitoring and recognition for the transformer winding pressing state.

Description

technical field [0001] The invention relates to a transformer winding state recognition method, in particular to a transformer winding state recognition method based on a fuzzy adaptive resonance neural network, and belongs to the technical field of state monitoring and fault diagnosis of power transformers. Background technique [0002] Transformer faults will have a great impact on the safe, stable and economical operation of the entire power grid. Short-circuit accidents are one of the most common accidents in the daily operation of transformers. For general transformers, weak short-circuit resistance is a common problem. The anti-short-circuit capability of the transformer is closely related to the state of the windings. Insufficient short-circuit resistance will directly lead to loosening of the windings and a decrease in the compression force. However, at present, there is no effective method for effectively monitoring the state of the winding at home and abroad, and i...

AI Technical Summary

Problems solved by technology

[0004] The structure of the transformer is complex, and the equipment itself has nonlinearity

Due to the many forms and causes of transformer faults, coupled with changes in equipment operating conditions, the vibration signal contains complex dynamic, non-stationary and nonlinear components.

Therefore, the vibration signal cannot be analyzed and processed directly through simple Fourier transform, and the characteristic quantity must be extracted through signal processing to judge the operating state of the transformer.

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