Converter fault diagnosis method based on kernel density estimation

Abstract

The invention relates to a converter fault diagnosis method based on kernel density estimation. The method comprises the steps of: performing pre-processing of collected data through cubic B-spline wavelet analysis based on a mallat algorithm to obtain samples with fault features; employing a KDE fault classifier to perform offline training to select better parameters of the fault classifier and accurately dividing the normal conditions and each type of fault condition included in the training samples, and using the better parameters into a classifier network to obtain the optimal parameters;implanting the classifier network with the optimal parameters into online simulation to perform real-time online monitoring fault diagnosis of an actual circuit; and allowing the classifier network with completion of optimal parameters to distinguish known fault type samples and normal samples, complete the location of the known fault types of faults and identify the unknown faults for achievementof circuit protection in a condition of generation of unknown types of faults. The converter fault diagnosis method based on kernel density estimation can determine the health condition of the converter more accurately and more reliably, and also can improve the efficiency of the fault diagnosis of the converter.

Description

technical field [0001] The invention relates to the technical field of power electronics, in particular to a converter fault diagnosis method based on kernel density estimation. Background technique [0002] With the development of power electronics technology, the power electronic converter, as a device for power electronic AC-DC conversion, completes the high-efficiency conversion and control of electric energy. Since the power electronic converter plays an indispensable role in the electrical field, it is particularly important whether the power electronic converter can operate normally. Traditional power electronic converter fault diagnosis methods include artificial neural network, support vector machine, fault dictionary method, etc. [0003] The artificial neural network method uses the interconnection of artificial neurons to establish the mapping relationship between input features and output results. Through the continuous correction of neurons and their correspon...

AI Technical Summary

Problems solved by technology

[0003] The artificial neural network method uses the interconnection of artificial neurons to establish the mapping relationship between input features and output results. Through the continuous correction of neurons and their corresponding structures (weights, deviations), each time the weights are updated in reverse transmission, All values ​​of the network need to be updated, its convergence speed is slow, and during the training process, there is a tendency to forget old samples when learning new samples, which is not conducive to accurate classification of faults

The support vector machine is relatively simple in calculation, but because it is susceptible to the noise of the sampling signal, it will cause misjudgment of the output result

The fault dictionary has strong anti-interference ability, but it needs a large fault sample to achieve good results

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