Detection and classification of internal faults in bipolar HVDC transmission lines based on K-means data description method

Abstract

This paper proposes a novel protection scheme for detection and classification of internal faults in bipolar HVDC transmission lines using the K-means data description (KMDD) method. In the proposed protection scheme, the inverter-side dc voltage and current signals are utilized. Relatively short-time windows are considered for these signals, and the sum values of data windows are calculated. In the preparation stage, for each dc fault type, the KMDD method is applied to some post-fault data generated in various conditions. Then, the obtained centroids and thresholds are used for detecting and classifying new internal dc faults even with unseen conditions. The performance of the proposed method is evaluated for 4320 internal and 2816 external fault cases in a 1000 km bipolar overhead HVDC transmission line under various conditions not seen in the preparation stage. Besides simplicity and low sampling frequency requirement of the proposed protection scheme, the obtained results show that it is fast and accurate enough for the internal dc faults, and also it is stable during the external ac faults and the pre-fault normal conditions.