A Fast Open-Circuit Fault Diagnosis Method for Large Wind Turbines with Parallel Power Converters

Power rating of offshore wind turbines is becoming increasingly larger and their fault-ride-through capabilities are essential to enhance system reliability. Parallel power converters are commonly used to fulfill the required large power capacity. However, classical fault detection methods for the unparalleled topology usually cannot be applied herein due to the presence of zero-sequence circulating current (ZSCC) in the underlying parallel power converters, which can obscure fault characteristics. In this work, we first reveal that ZSCC induces fluctuations in phase current residuals when an open-circuit fault occurs, rendering the traditional residual threshold-based fault diagnosis ineffective. Then, we derive that the ratio of fault-to-healthy phase current residuals remains approximately at a fixed proportion despite ZSCC disturbances. Using this feature, we propose a fault diagnosis method for parallel power converters in wind turbine systems. The method achieves dynamic residual ratio extraction through sliding-window root-mean-square normalization and convolutional signature enhancement, resulting in self-adaptive threshold calibration and a very short detection time of several sampling cycles. Experimental and simulation data verify the effectiveness of the proposed diagnosis method at various operation scenarios.