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DC microgrids (DCMGs) are gaining popularity mainly in shipboards and isolated power systems for their modest control and higher efficiency. However, the newer conception and less literature are forcing the operators to take up tough challenges for smoother operation of the overall system. Protection areas of DCMGs are still immature because of the inexperience of handling DC faults and less availability of DC circuit breakers (DCCBs) in outsized range. In this work, the fault detection and classification issue in low-voltage-DC-microgrid (LVDCMG) has been solved with a bilayered machine learning scheme based on weighted K-nearest neighbor (WKNN) and decision tree (DT). WKNN accurately detects the fault in the line, and DT classifies the fault as PG or PP for further corrective measures. The developed technique has been implemented in an LVDCMG with PV and battery energy sources. Voltage and current measurements from the network have been used as training samples to the classifier models. It has been noticed that the proposed bilayered protection scheme is giving 100% detection accuracy. The outcome of the proposed structure seems to be promising and has been compared with some of the existing machine learning (ML)-based fault detection techniques for microgrids (MGs). [ABSTRACT FROM AUTHOR]

Faster fault detection and location has become one of the prime assignments in microgrids for better control strategies. Quick identification of fault may result in location estimation tougher due to insufficient measurement data. In this article, a better scheme for fault detection and a novel method for the location of fault with good accuracy are portrayed. The speed and cost‐effectiveness of the proposed scheme has been assured due to less sampling frequency. The proposed method is developed for a standalone low voltage DC (LVDC) system with photovoltaics and battery energy storage system. A large number of fault scenarios have been simulated and analyzed by varying faults (pole‐pole, pole‐ground), fault resistance, and locations. The proposed scheme deals with local measurements that heartens the easy implementation of the proposed method. The case studies have been simulated in MATLAB/ SIMULINK environment. The efficacy of the proposed protection scheme is further validated in a real‐time environment. [ABSTRACT FROM AUTHOR]

Summary: Modern power system operates in stressed conditions due to fickle loads, high penetration of renewables, and complex control strategies. The failure of any power system asset may lead to protract power interruption or even catastrophic failures. Synchrophasor‐based extensive research advancements in wide area measurement, protection, and control (WAMPAC) systems are solving critical operation and control problems of the power system. This paper proposes a new combinatorial methodology for monitoring the prime power system components in preference to complete observability of the system. A multicriteria decision support (MCDS) approach, analytical hierarchy process (AHP), has been used to prepare a ranking according to component bus priority for synchrophasor measurement unit installation. The priority list is further processed with intelligent search technique (IST) to compute the optimal solution for synchrophasor meter placement, ensuring complete observability, maximum measurement redundancy, and coverage of prime system components with direct monitoring. Some practical contingencies related to synchrophasor meters placement problem like single meter loss or line outage have also been considered for the case studies. The efficiency of the proposed method has been verified with IEEE 14‐bus, 24‐bus, 30‐bus, 57‐bus, and 118‐bus and New England 39‐bus test systems. To prove the superiority of the method, in essence of synchrophasor meter placement problems, the outcome has been compared with some of the existing methods from the literature. [ABSTRACT FROM AUTHOR]