A wind power converter open circuit and current sensor fault diagnosis method

CN121831518BActive Publication Date: 2026-06-09HOHAI UNIV

Patent Information

Authority / Receiving Office: CN · China
Patent Type: Patents(China)
Current Assignee / Owner: HOHAI UNIV
Filing Date: 2026-03-13
Publication Date: 2026-06-09

AI Technical Summary

Technical Problem

Existing technologies struggle to accurately identify IGBT open-circuit faults and current sensor faults in wind power converters, especially under wind speed fluctuations and noise interference, leading to overlapping fault characteristics, misdiagnosis, or missed diagnosis. Furthermore, existing methods increase hardware costs or rely on precise system parameters, resulting in insufficient robustness.

Method used

Adaptive Noise Complete Ensemble Empirical Mode Decomposition (CEEMDAN) is used to decompose the three-phase current signal at multiple scales, select discriminative Intrinsic Mode Functions (IMFs), construct an adaptive hybrid topology graph structure, and achieve joint fault diagnosis through a spatiotemporal feature graph convolutional network to extract composite features that fuse time and frequency information.

Benefits of technology

Under conditions of wind speed fluctuations and noise interference, it can accurately distinguish between IGBT open circuit faults and current sensor faults, reduce false alarm rates, improve the stability and reliability of diagnosis, meet the needs of online monitoring and fault early warning, and enhance the operational safety of wind turbine units.

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Abstract

The application provides a wind power converter open circuit and current sensor fault diagnosis method, and belongs to the technical field of wind turbine power electronic conversion and fault diagnosis. The technical problems that the current signal is non-stationary due to wind speed fluctuation, and the fault characteristics of the converter insulated gate bipolar transistor (IGBT) open circuit and the current sensor are similar and easy to confuse and difficult to accurately identify are solved. The technical scheme comprises the following steps: S1, collecting three-phase current signals of a machine side of a wind turbine and performing pretreatment; S2, obtaining a plurality of intrinsic mode functions (IMFs) and a residual component; S3, obtaining a characteristic vector set of different fault states; S4, obtaining an adjacency matrix describing the correlation between characteristics; and S5, extracting a fault classification result. The application enhances the distinguishing ability of similar faults, reduces the misjudgment rate, and improves the diagnosis accuracy and robustness.

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