Advanced fault location in MTDC networks utilising optically-multiplexed current measurements and machine learning approach. (April 2018) - Tzelepis, D.; Dyśko, A.; Fusiek, G.; Niewczas, P.; Mirsaeidi, S.; Booth, C.; Dong, X.
Highlights: Novel and accurate fault location algorithm for DC fault location in MTDC networks. Adoption of machining learning approach for reduction of errors in fault location calculation. Wavelet transform for detection of travelling waves and signal de-noising. The simulated response of DC current sensors experimentally validated. Abstract: This paper presents a method for accurate fault localisation of DC-side faults in Voltage Source Converter (VSC) based Multi-Terminal Direct Current (MTDC) networks utilising optically-multiplexed DC current measurements sampled at 5 kHz, off-line continuous wavelet transform and machine learning approach. The technical feasibility of optically-based DC current measurements is evaluated through laboratory experiments using commercially available equipment. Simulation-based analysis through Matlab/Simulink® has been adopted to test the proposed fault location algorithm under different fault types and locations along a DC grid. Results revealed that the proposed fault location scheme can accurately calculate the location of a fault and successfully identify its type. The scheme has been also found to be effective for highly resistive fault with resistances of up to 500 Ω . Further sensitivity analysis revealed that the proposed scheme is relatively robust to additive noise and synchronisation errors.