An Enhanced Differential Protection Scheme for LVDC Microgrid

Abstract

The low-voltage dc (LVDC) microgrid possesses numerous benefits and their penetration in the power system has increased rapidly in recent years. However, the detection of faults in the LVDC microgrid is a challenging issue due to the large magnitude of fault currents and fault-level variation in the microgrid. The performance of the recent current and its derivative-based protection scheme is limited in case of faults in the islanding mode of operation, different microgrid topologies, varying distributed generations (DGs) penetration, and against the measurement noise. This article presents an enhanced differential protection scheme for LVDC microgrid integrated with multiple DGs and storage. The differential current and its first derivative are processed through the decision tree (DT) algorithm for fault detection and the K-nearest neighbor (KNN) technique is utilized for fault classification. The robustness of the proposed protection scheme is tested for different fault types and fault conditions with variation in microgrid topology and operating conditions. The impact of the intermittent and volatile nature of the DGs, the presence of measurement noise, and assessment during external faults and critical no-fault cases have been investigated. The proposed scheme is tested on the MATLAB/ SIMULINK and validated on the Typhoon HIL platform for the assessment of real-time performance. The test results show that the proposed scheme can detect and classify faults with high accuracy and faster response time and, thus, can be a potential candidate for providing dependable protection measures for LVDC microgrids.