Intelligent fault location in MTDC networks by recognising patterns in hybrid circuit breaker currents during fault clearance process

In this paper, a novel, learning-based method for accurate location of faults in MTDC networks is proposed. By assessing the DC circuit breaker currents during the fault clearance process, a pattern recognition approach is adopted from which the fault location is estimated. The implementation of the algorithm is allocated into three main stages, where similarity coefficients and weighted averaging functions (incorporating exponential kernels) are utilized. For the proposed algorithm, only a short-time window of data (equal to 6 ms) is required. The performance of the proposed method is assessed through detailed transient simulation using verified MATLAB/Simulink models. Training patterns have been retrieved by applying a series of different faults within an MTDC network. Simulation and experimental results revealed that the proposed scheme i) can reliably determine the type of fault ii) can accurately estimate the fault location (including the cases of highly resistive faults) and iii) is practically feasible.