Evaluation of a Local Fault Detection Algorithm for HVDC Systems

A great increase in the amount of energy generated from clean and renewable sources integrated in the electric power system is expected worldwide in the coming years. High Voltage Direct Current (HVDC) systems are seen as a promising alternative to the traditional Alternating Current (AC) systems for the expansion of the electric power system. However, to achieve this vision, there are some remaining challenges regarding HVDC systems which need to be solved. One of the main challenges is related to fault detection and location in HVDC grids. This paper reviews the main protection algorithms available and presents the evaluation of a local fault detection algorithm for DC faults in a multi-terminal Voltage Source Conversion (VSC) based HVDC grid. The paper analyses the influence of the DC voltage sampling frequency and the cable length in the performance of the algorithm. © 2019, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ).The authors thank the support from the Spanish Ministry of Economy, Industry and Competitiveness (project ENE2016-79145-R AEI/FEDER, UE) and GISEL research group IT1083-16), as well as from the University of the Basque Country UPV/EHU (research group funding PPG17/23)

Fault detection based on ROCOV in a multi-terminal HVDC grid

Protection of a meshed VSC-HVDC grid is a challenge due to the behaviour of DC current and voltage signals during fault conditions. Protection systems must operate in a very short time range. Since fault detection should be very fast, local measurement based algorithms are mostly used; communication based algorithms lack the needed speed as a result of the communication time delay. This way, a ROCOV algorithm is proposed in this paper. This algorithm is analysed for different fault conditions.The authors gratefully acknowledge the support from the Spanish Ministry of Economy, Industry and Competitiveness (project ENE2016-79145-R AEI/FEDER, UE), the Basque Government (GISEL research group IT1191-19), as well as from the University of the Basque Country UPV/EHU (research group funding GIU18/181)