Method for diagnosing fault of transformer on basis of clustering algorithm and neural network

Abstract

The invention discloses a method for diagnosing a fault of a transformer on the basis of a clustering algorithm and a neural network. The method comprises the following steps that (a) the type of the fault is determined according to an IEC standard and a DL / T722-2000 guideline, and the characteristic quantities of a fault sample set are selected from an original sample set; (b) clustering is carried out on samples by utilizing a K-means clustering method; (c) an RBF neural network is established; (d) parameter learning is carried out to determine the number, the center position, the width and the output weight of hidden layers; (e) optimization training is carried out by adopting PSO to determine the positions of the centers of the hidden layers, and the number, the width and the weight of the hidden layers are determined by utilizing a test method, a minimum distance method and a pseudo-inverse method respectively; (f) training samples are input, the posterior probability is solved, and the type of the fault is judged. According to the method for diagnosing the fault of the transformer on the basis of the clustering algorithm and the neural network, the training samples and the test samples can be evenly divided from the total samples, more complete test on the neural network can be carried out by good test samples, and therefore the neural network can be evaluated correctly and reasonably.

Description

technical field [0001] The invention relates to a fault diagnosis method for an oil-immersed transformer, in particular to a fault diagnosis method for an oil-immersed transformer based on the combination of clustering algorithm and PSO-RBFNN. Background technique [0002] With the continuous development of my country's national economy, the demand for electricity is also increasing. The entire power system is also developing towards higher voltage levels and greater transmission capacity, and power transformers are one of the most important equipment in the system, and their operating status directly affects the safety and stability of the entire power system. Due to the complexity of the internal structure of the power transformer and the particularity of the operating environment, failures are inevitable during the long-term operation of the transformer. Once a fault occurs, it will seriously affect the social production and people's life, and cause serious economic loss...

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Problems solved by technology

However, this method often cannot obtain the optimal central parameters, and it is easy to converge to the local optimal value.

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