Power electronic circuit fault diagnosis method based on longicorn beard optimization deep belief network algorithm

Abstract

The invention discloses a power electronic circuit fault diagnosis method based on a longicorn beard optimization deep belief network algorithm, and the method comprises the steps: collecting a DC side bus output current signal of a three-phase PWM rectifier in an open-circuit fault mode of different switching devices, and enabling the DC side bus output current signal to serve as an original dataset; utilizing empirical mode decomposition to extract intrinsic mode function components of output current signals under different switching device open-circuit fault modes, and constructing an original fault feature set; making feature selection based on an extreme tree algorithm to generate a fault state sensitive feature set; optimizing the structure of the deep belief network by adopting a longicorn beard algorithm; and training the optimized deep belief network by using the training set, and performing verification by using the test set to obtain a fault identification result. Accordingto the method, a feature extraction algorithm, an optimization algorithm and a deep learning classification algorithm are combined, so that the feature data volume and classification accuracy of power electronic circuit fault diagnosis are greatly improved.

Description

technical field [0001] The invention relates to a fault diagnosis method for a power electronic circuit, in particular to a fault diagnosis method for a power electronic circuit based on a beetle beetle optimized deep belief network algorithm. Background technique [0002] As a new basic subject of comprehensive application technology, power electronics technology is expanding its application fields with the advancement and development of technology. At present, power electronics technology can be seen in the fields of national defense, military, aerospace, power conversion and transmission, and information communication. use of the device. Among them, the power electronic circuit, as an important part of the power electronic device, is mainly composed of the main circuit and the control circuit. In actual work, the failure probability of the main circuit is much higher than that of other components. Faults may lead to abnormal working status of the entire system and device...

AI Technical Summary

Problems solved by technology

Analytical model fault diagnosis method can be divided into state estimation fault diagnosis and parameter estimation fault diagnosis. This method needs to accurately establish the fault model of the circuit to be diagnosed; signal recognition method is a fault diagnosis method based on signal processing. Its biggest feature is There is no need to establish an accurate diagnostic model of the diagnosed circuit, and it has strong self-adaptive ability. Select the appropriate circuit output to analyze the fault information contained in it. The commonly used processing methods include Fourier transform method, Park transform method and wavelet transform method , but the result of signal processing by this method may not have actual physical meaning, and the process may lead to the loss of effective fault information, or when there are many fault types, the fault feature quantity selected after transformation cannot effectively distinguish different fault types; based on The fault diagnosis method of knowledge fusion is another branch of fault diagnosis method developed in the field of power electronic circuit fault diagnosis in recent years, such as the artificial neural network method, but this shallow learning network has problems when solving complex high-dimensional data. limitation

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