Inverter IGBT tiny fault feature extraction method based on multi-modal data

Abstract

The invention relates to an inverter IGBT tiny fault feature extraction method based on multi-modal data. The method comprises the following steps: 1), building an equivalent second-order system of a transient mode of a two-level traction inverter, and carrying out the parameter identification of an expression of the equivalent second-order system; 2) carrying out modal division on the output voltage data of the inverter through an Elman neural network; 3) extracting corresponding tiny fault characteristic parameters in each transient mode, wherein the tiny fault characteristic parameters comprise a voltage overshoot sigma, peak time tp and a voltage slope absolute value delta; 4) extracting a voltage steady-state value Vstab in each steady-state mode; and 5) carrying out fault detection according to the extracted characteristic parameters of the IGBT tiny fault. Compared with the prior art, the method has the advantages of easy realization, cost saving, noise interference reduction, modal classification accuracy improvement, accurate extraction, fault detection and the like.

Description

technical field [0001] The invention relates to the field of inverter fault detection, in particular to an inverter IGBT micro fault feature extraction method based on multi-modal data. Background technique [0002] During the long-term operation of the IGBT in the commonly used two-level traction inverter, due to the cumulative effect of voltage and current, the interference of line electromagnetic waves, and the thermal stress generated at the interface due to the different thermal expansion coefficients of the materials in various parts of the module, thus Minor faults such as bonding wire breakage and solder layer fatigue will occur. If these minor faults are not detected in a timely and effective manner and the faults are allowed to develop, they will eventually evolve into significant faults such as short circuit or open circuit, which will adversely affect the operation of the system. Compared with significant faults, IGBT micro faults have the characteristics of smal...

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Problems solved by technology

[0003] In recent years, experts and scholars at home and abroad have conducted extensive and in-depth research on IGBT micro-fault detection methods, and have identified some fault characteristic variables that can clearly characterize IGBT micro-faults, including the collector-emitter saturation conduction voltage drop V CE(on) , gate voltage V GE , turn-on threshold voltage V GE(th) , off time t off , opening time t on , junction-to-case thermal resistance R th , junction temperature T j etc., but these known fault characteristic variables need to be obtained by installing corresponding sensors on the IGBT module inside the inverter, which not only increases the complexity of the system, but also increases the difficulty of sampling, making it difficult to obtain fault characteristics in time information

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