  Protection against dc faults is one of the main technical hurdles faced when
operating converter-based HVdc systems. Protection becomes even more
challenging for multi-terminal dc (MTdc) systems with more than two
terminals/converter stations. In this paper, a hybrid primary fault detection
algorithm for MTdc systems is proposed to detect a broad range of failures.
Sensor measurements, i.e., line currents and dc reactor voltages measured at
local terminals, are first processed by a top-level context clustering
algorithm. For each cluster, the best fault detector is selected among a
detector pool according to a rule resulting from a learning algorithm. The
detector pool consists of several existing detection algorithms, each
performing differently across fault scenarios. The proposed hybrid primary
detection algorithm: i) detects all major fault types including pole-to-pole
(P2P), pole-to-ground (P2G), and external dc fault; ii) provides a wide
detection region covering faults with various fault locations and impedances;
iii) is more robust to noisy sensor measurements compared to the existing
methods. Performance and effectiveness of the proposed algorithm are evaluated
and verified based on time-domain simulations in the PSCAD/EMTDC software
environment. The results confirm satisfactory operation, accuracy, and
detection speed of the proposed algorithm under various fault scenarios.
