Abstract
Fast and accurate identification of short-circuit faults is important for post-fault service restoration and maintenance in DC distribution grids. Yet multiple power sources and complex system topologies complicate the fault identification in multi-terminal DC distribution grids. To address this challenge, this paper introduces an approach that achieves fast online identification of both the location and the severity of faults in multi-terminal DC distribution grids. First, a generic model describing the dynamic response of DC lines to both pole-to-ground and pole-to-pole faults with fault currents injected from both line ends is developed. On this basis, a Kalman filter is adopted to estimate both the fault location and resistance. In the real-time simulation of various fault scenarios in a three-terminal DC distribution grid model with Opal-RT platform, the proposed method is proved to be effective with a short response time of less than 1 ms.
Highlights
Conventional power systems are undergoing a profound transition led by emerging DC technologies
In order to verify the performance of the proposed fault identification method in multi-terminal DC (MTDC) distribution grids, different fault scenarios in a three-terminal DC system were simulated with Opal-RT real-time simulator
This paper introduces an online fault identification method for MTDC distribution grids, which is based on the parameter estimation in monitored DC lines
Summary
Conventional power systems are undergoing a profound transition led by emerging DC technologies. MTDC distribution grids are in general small-scale networks with short feeder lines In such systems, the time delays of traveling waves are difficult to measure. The general problem of these supervised machine learning methods is that their performances are highly dependent on the availability of labeled training data, especially fault data, which are difficult to acquire and only sparsely available in real-world power systems As for those models trained with simulation data, their effectiveness in realistic fault conditions is not verified. (1) The proposed fault identification method can cover both PG and PP faults in DC lines with single- or double-ended fault current injection, which has improved applicability in the protection of MTDC distribution grids. (3) Using the Kalman filter-based parameter estimation algorithm, the proposed method can achieve fast fault identification with a short response time of less than 1 ms.
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