State Monitoring and Fault Diagnosis of HVDC System via KNN Algorithm with Knowledge Graph: A Practical China Power Grid Case

Abstract

Based on the four sets of faults data measured in the practical LCC-HVDC transmission project of China Southern Power Grid Tianshengqiao (Guangxi Province, China)–Guangzhou (Guangdong Province, China) HVDC transmission project, a fault diagnosis method based on the K-nearest neighbor (KNN) algorithm is proposed for an HVDC system. This method can effectively and accurately identify four different fault types, aiming to contribute to construction of a future HVDC system knowledge graph (KG). First, function and significance of fault diagnosis for KG are introduced, along with four specific fault scenarios. Then, the fault data are normalized, classified into a training set and a test set, and labeled. Based on this, the KNN fault diagnosis model is established and Euclidean distance (ED) is selected as the metric function of the KNN algorithm. Finally, the training data are conveyed to the model for training and testing, upon which the diagnosis result obtained by the KNN algorithm with a knowledge graph is compared with that of the support vector machine (SVM) algorithm and Bayesian classifier (BC). The simulation results show that the KNN algorithm can achieve the highest diagnosis accuracy, with more than 83.3% diagnostic accuracy under multiple test sets among all three diagnosis methods.