Distribution transformer fault diagnosis method with automatic feature mining and automatic parameter optimization

Abstract

The invention relates to a distribution transformer fault diagnosis method with automatic feature mining and automatic parameter optimization. The distribution transformer fault diagnosis method comprises the following steps of: installing a vibration signal acquisition device on a distribution transformer, and acquiring a vibration waveform of the distribution transformer during operation from the vibration signal acquisition device; constructing a distribution transformer fault feature extraction model based on a stack auto-encoder after secondary tuning; extracting a vibration signal feature vector yn by using the stack auto-encoder after secondary tuning, labeling corresponding features, and establishing a database containing normal and various faults; segmenting a data set, namely splitting the data set into a training set and a test set according to the proportion of X1: X2; training a random forest classifier by using the training set; and based on the network parameters of thetrained random forest classifier, establishing a distribution transformer fault diagnosis model to realize distribution transformer fault diagnosis. According to the distribution transformer fault diagnosis method, the accuracy of fault diagnosis of the distribution transformer can be remarkably improved, and the method has good robustness and outstanding diagnosis performance.

Description

technical field [0001] The invention relates to a fault diagnosis method for distribution transformers with automatic feature mining and parameter automatic optimization. Background technique [0002] The transformer in the power distribution system is an extremely important power transmission and transformation equipment in the power transformation and transmission link of my country's power system, and the distribution transformer used by users is a kind of terminal power transmission and transformation equipment for users, with a large number and a wide range of applications. Reliable operation is crucial to the stable and safe operation of the entire power grid. Once a transformer fails, it may cause huge economic losses to users. According to relevant statistics, the faults of transformer windings account for more than half of the total transformer accidents in our country. Therefore, it is extremely necessary to monitor the normal working status and reliability of the ...

AI Technical Summary

Problems solved by technology

These feature quantities can describe the morphological characteristics of the vibration signal to a certain extent, but the effect of practical application is not ideal, and it is easily affected by factors such as disturbance and noise.

[0004] Over the years, through the efforts of researchers, some progress has been made in vibration signal acquisition, feature signal extraction, and fault detection model establishment. However, the transformer online detection method based on vibration analysis method is still immature, and more in-depth research is still needed.

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