Author:
Bui Duong Minh,Le Duy Phuc,Nguyen Hieu Minh
Abstract
A direct-current microgrid (MG) can be susceptible to extremely high fault currents contributed by the output filter capacitors of power converters and can also face protection challenges because of the non-zero crossing of fault currents. In a Low-Voltage Direct Current (LVDC) MG, low-fault-tolerance converters such as boost converters and bidirectional converters mostly require a fast and adaptable fault protection scheme that can detect and clear quickly faults irrespective of a wide range of fault impedances in the system. Several current- and voltage-derivative-based protection methods with communication support have been developed to primarily protect DC MGs due to their high sensitivity and selectivity. Over-current and under-voltage-based protection schemes are mostly suggested as backup protections for the DC MGs. To accurately detect and rapidly clear the faults even in the case of communication failure from the primary protection, this paper proposes a novel backup fault protection scheme with high selectivity, adaptability, and scalability for islanded LVDC MGs based on local measurements along with Chi-square-distribution-based statistics. Specifically, this developed backup protection not only applies a cumulative summation methodology for the locally measured signals to extract derivative and integral characteristics of the current and voltage, but also uses the Chi-square-distribution-based statistics to consistently calculate tripping thresholds for the effective detection of different fault events in the LVDC MG, regardless of variable fault resistances and the communication-link damage. As a result, the proposed backup protection is capable of accurately detecting various DC faults to secondarily protect the source and load branches of the system within the expected time frame of a few milliseconds and has been validated through multiple staged fault tests from an off-grid and ungrounded 1kW and 48VDC MG testbed.
Publisher
Engineering, Technology & Applied Science Research