A Novel Ultra-High Voltage Direct Current Line Fault Diagnosis Method Based on Principal Component Analysis and Kernel Density Estimation

As renewable energy resources are increasingly deployed on a large scale in remote areas, their share within the power grid continues to expand, rendering direct current (DC) transmission essential to the stability and efficiency of power systems. However, existing transmission line protection principles are constrained by limited fault feature quantities and insufficient correlation exploration among features, leading to operational refusals under remote and high-resistance fault conditions. To address these limitations in traditional protection methods, this study proposes an innovative single-ended protection principle based on Principal Component Analysis (PCA) and Kernel Density Estimation (KDE). Initially, PCA is employed for multidimensional feature extraction from fault data, followed by KDE to construct a joint probability density function of the multidimensional fault features, allowing for fault type identification based on the joint probability density values of new samples. In comparison to conventional methods, the proposed approach effectively uncovers intrinsic correlations among multidimensional features, integrating them into a comprehensive feature set for fault diagnosis. Simulation results indicate that the method exhibits robustness across various transition resistances and fault distances, demonstrates insensitivity to sampling frequency, and achieves 100% accuracy in fault identification across sampling time windows of 0.5 ms, 1 ms, and 2 ms.