Robust Open Circuit Fault Diagnosis Method for Converter Using Automatic Feature Extraction and Random Forests Considering Nonstationary Influence

Data-driven methods have shown promising performance in power converter fault diagnosis. However, the existing methods are bothered by the inadequacy of accuracy and robustness, due to the feature variation in the different operating points, especially in nonstationary fault signals. This article proposes a robust data-driven fault diagnosis method combining automatic feature extraction (AFE) and improved random forests (IRFs). In AFE, this method uses optimal discrete wavelet transform, optimized by the defined information component function, to extract multiresolution time-frequency features from nonstationary fault signals, and the improved ReliefF algorithm is used to select important fault features considering both the independent and interactive weights to overcome nonstationary influence. To enhance the robustness, the IRFs adjusts the weight of decision trees by robustness testing based on out-of-bag data, which extracts the mapping relationship between fault features and fault labels for fault location. The comparative experimental results show that the proposed method achieves 99.94% fault diagnosis accuracy, and has superior fault diagnosis robustness compared to existing methods.