Fault Diagnosis Method for High-Voltage Direct Current Transmission System Based on Multimodal Sensor Feature-LightGBM Algorithm: A Case Study in China

To improve/enhance the intelligence and accuracy of fault diagnosis in high-voltage direct current (HVDC) systems, this paper proposes a fault diagnosis model for HVDC systems based on the multimodal sensor feature-light gradient boosting machine (MSF-LightGBM) algorithm. First, a sample set encompassing four typical types of faults, namely alternating current (AC) faults, direct current (DC) faults, inverter commutation failures, and converter valve faults, was constructed based on the actual HVDC transmission data from China. Second, considering the issues of imbalanced sample classes and a relatively small sample size in the original dataset, a data augmentation method incorporating multiple types of noise is introduced to improve the diversity and practical representativeness of the samples. Then, time-series features in the time domain, frequency domain, and wavelet domain, along with Pearson correlation features among 15 sensors, are extracted to form a comprehensive feature vector. On this basis, automatic feature selection is performed using recursive feature elimination (RFE) to screen out the key features. Finally, the paper builds an optimized LightGBM classification model is built using the key features. Through comparative experiments with five machine learning methods, the results indicate that the accuracy of the proposed method on the test set reaches 0.9583, significantly outperforming the other comparison models. The receiver operating characteristic (ROC) curve analysis reveals that the average area under the curve (AUC) for all four types of faults is 0.975, validating the stability and accuracy of the proposed model in multi-class fault identification.