Machine Learning Based One-Terminal Fault Areas Detection in HVDC Transmission System

Abstract

Due to low sensitivity in existing High-Voltage Direct Current (HVDC) fault detection methods and difficulty in identifying high-resistance grounding faults, this paper presents two signal-terminal HVDC transmission system fault detection methods based on machine learning. The waveform of the fault voltage collected by the rectifier side detection device is directly used as the input data of K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), eliminating the cumbersome process of fault signal processing. Training is performed in various fault areas and fault types. Then fault areas will be detected by the trained KNN and SVM models. A ± 500 kv HVDC transmission line model was built by electromagnetic transient simulation software PSCAD/EMTDC to simulate and compare different fault areas and fault types. Testing results show that the proposed method can reliably and accurately detect faults with a resistance up to $1000 \Omega$.