Flexible DC distribution line protection principle based on distance metric of clustering centres

Abstract

Aiming at the issue of unsatisfactory selectivity of single-terminal protection, and poor speed and high requirements for data synchronization of double-terminal protection in flexible DC distribution networks, an artificial intelligence (AI)-based method is proposed to obtain the best clustering centres through unsupervised clustering analysis of historical data to realize the non-deterministic flexible DC distribution line protection principle. The method forms historical data samples by simulating different types of short-circuit faults at different locations of the line in advance, combining them with the actual faults, and collecting and processing the post-fault currents. The K-means clustering algorithm is then used to find the best clustering centres corresponding to different fault types, and fault identification and pole selection are realized by comparing the distance between real-time data and each clustering centre. The process relies merely on single-terminal current as the characteristic quantity, and it does not need complicated feature extraction and calculation. Thus, the cumbersome threshold setting in conventional current protections can be avoided. Finally, the case studies are carried out in PSCAD/EMTDC, and the results show that the proposed protection has good selectivity and rapidity, and the tolerance to fault resistance is improved compared with the conventional local-current-based protections.