A fault prediction method based on characteristic quantity optimization and a wavelet kernel function LSSVM

Abstract

The invention discloses a fault prediction method based on characteristic quantity optimization and a wavelet kernel function LSSVM, and relates to the technical field of fault prediction of power transformers. The method comprises the following steps: firstly, acquiring a ratio of DGA characteristic quantity, secondly, establishing a support vector machine model, and selecting a radial basis function as a kernel function of a classification model; Then, encoding the candidate characteristic quantity and the penalty factor c kernel parameter of the SVM classification model to the same chromosome, optimizing the chromosome by adopting a genetic algorithm, wherein the optimal characteristic quantity is the optimal chromosome selected from the genetic algorithm; taking the optimal feature combination as the input of the next fault prediction and diagnosis function; establishing a wavelet kernel function-least squares support vector machine prediction model; And optimizing the penalty factor of the prediction model and the kernel parameters of the wavelet kernel function by using an imperialism competition algorithm to obtain an optimal parameter combination, and constructing an optimal prediction model based on the parameter combination. Operation state analysis and fault prediction of the transformer at a future moment are realized.

Description

technical field [0001] The invention belongs to the technical field of electrical equipment fault diagnosis and prediction methods, and in particular relates to a fault prediction method based on feature quantity optimization and wavelet kernel function LSSVM. Background technique [0002] At present, the large-capacity transformers in the power grid are generally oil-immersed transformers, and the stable operation of the transformer is related to the safe and stable operation of the power system. Therefore, there should be corresponding technology for on-line monitoring of oil-immersed transformers, and by analyzing the corresponding data to evaluate the operating status of the transformer or the aging status of the oil-paper insulation, this will greatly reduce unnecessary maintenance costs and meet economical requirements. sustainable development. [0003] At present, dissolved gas analysis (DGA) is a common transformer fault diagnosis technology. Its main principle is: ...

AI Technical Summary

Problems solved by technology

Since the gas produced by different faults is different, the corresponding transformer fault can be judged according to the content of dissolved gas in the oil, but the content of dissolved gas in these oils will also be affected by factors such as temperature and moisture in the transformer

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