Double Open-circuit Faults Research Articles

Open-Switch Fault Diagnosis of Three-Phase PWM Converter Systems for Magnet Power Supply on EAST

The three-phase two-level pulsewidth modulation (PWM) converter operates in rectification state or active inverter state according to the corresponding experimental requirements of Experimental Advanced Superconducting Tokamak. However, the current fault diagnosis methods proposed for PWM converters usually deal with only the rectification state or inverter state. To solve this problem, this article proposes a diagnosis method to detect single and double open-circuit faults under both the rectification state and active inverter state. First, an inception-based neural network diagnosis model is proposed, in which 1 × 1, 3 × 3, 5 × 5, and 7 × 7 convolution and 3 × 3 max-pooling processes are conducted in parallel at the input and the output and stacked together to generate the final output. Second, the model is trained using three-phase alternating current current signals to determine the location of open-switch faults with a large number of simulation data samples and fewer experimental data samples. Finally, the experimental result shows that the proposed model can accurately detect approximately 99.14% of the open-switch faults within 12.83 ms (<3/4 cycle) without an additional sensor. Furthermore, the result shows that the proposed model is robust to load voltage transient conditions and nonunity power factor conditions.

Open-Circuit Faults Diagnosis in Direct-Drive PMSG Wind Turbine Converter

The condition monitoring and fault diagnosis have been identified as the key to achieving higher availabilities of wind turbines. Numerous studies show that the open-circuit fault is a significant contributor to the failures of wind turbine converter. However, the multiple faults combinations and the influence of wind speed changes abruptly, grid voltage sags and noise interference have brought great challenges to fault diagnosis. Accordingly, concerning the open-circuit fault of converters in direct-driven PMSG wind turbine, a diagnostic method for multiple open-circuit faults is proposed in this paper, which is divided into two tasks: The first one is the fault detection and the second one is the fault localization. The detection method is based on the relative current residuals after exponential transformation and on an adaptive threshold, and the localization method is based on the average values of fault phase currents. The scheduled diagnosis method is available to both the generator-side converter and the grid-side converter, allowing to detect and locate single and double open-circuit faults. For validating this, robustness test and multiple open-circuit faults diagnosis are presented in a 2-MW direct-driven PMSG wind turbine system, the results validate the reliability and effectiveness of the proposed method.

Fault Diagnosis of Wind Turbine Power Converter Considering Wavelet Transform, Feature Analysis, Judgment and BP Neural Network

The larger capacity of power converters increases with the size of wind turbines over the years, which implies more failures occur in the components of power converter. Meanwhile, the strategies of fault diagnosis of power converter in wind turbine are still under discussion and research. The main purpose of this paper is proposing a strategy containing wavelet transform, feature analysis, judgment and back propagation neural network (BPNN) classification (WT-FA-JD-BP) to identify the single and double power components open-circuit faults accurately, happen in grid-side converter (GSC) of permanent magnet synchronous generator (PMSG) wind turbine systems. First, as the original signals, the three-phase bridge legs voltage of the GSC are collected under different faults. Second, wavelet transform is used to decompose and reconstruct the signals. Third, a new method includes feature analysis and judgment is conducted to amplify the divergence of data obtained by wavelet transform. Finally, the data are used as the inputs of BPNN for decision-making and classification. The simulation and experimental results show that the proposed strategy can classify the single and double open-circuit faults, and the accuracy is higher than that without data feature analysis and judgment.