Ensemble learning-based electric power electronic switch device network fault diagnosis method

Abstract

An ensemble learning-based electric power electronic switch device network fault diagnosis method includes the following steps of: (1) collecting an output voltage or current signal vector set {Vn<q>}, n=1,2...N of an electric power electronic circuit in different switch device fault modes; (2) using principal component analysis to extract normalized fault characteristic vectors in the fault mode Fq from a signal vector Vn<q>, and obtaining a normalized fault characteristic vector set shown in the description according to the normalized fault characteristic vectors shown in the description; (3) using the normalized fault characteristic vector set shown in the description to train k neural network element classifiers in turn, and setting the number limitation K of the neural network element classifiers as 50 and a system error threshold e0; and (4) repeating steps (1) and (2) aiming at the circuits to be detected, obtaining a fault characteristic vector V* to be detected, enabling the fault characteristic vector to access the trained k neural network element classifiers, and using an ensemble learning method to obtain an ensemble recognition result. The ensemble learning-based electric power electronic switch device network fault diagnosis method can avoid defects of over learning of a single neural network and falling in local minimum, improves the classification precision of the neural network element classifiers.

Description

technical field [0001] The invention relates to the field of fault diagnosis of power electronic circuits, in particular to a fault diagnosis method for a power electronic switching device network based on integrated learning. Background technique [0002] In power electronic systems, power electronic switching devices such as thyristors, MOSFETs (Metal Oxide Semiconductor Field Effect Transistors) and IGBTs (Insulated Gate Bipolar Transistors), etc., are considered to be the most frequently failed components. Switching faults include short-circuit faults and open-circuit faults. When a short-circuit fault occurs, the overcurrent will damage other switches, components and loads in the system; when an open-circuit fault occurs, the system cannot achieve the intended function; in addition, due to current imbalance The resulting pulsed signal can generate noise and vibration in the load or motor. Therefore, research on fault diagnosis technology for power electronic switching ...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to overcome the deficiencies of the above-mentioned background technology, provide a network fault diagnosis method for power electronic switching devices based on integrated learning, avoid over-learning of a single neural network and the defects of falling into a local minimum, and improve the accuracy of the neural network. Classification Accuracy of Primitive Classifiers

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