End-to-end microgrid protection using distributed data-driven methods

This paper introduces an end-to-end microgrid protection framework that offers real-time system monitoring, fault-related decision making, and circuit breaker control. This is achieved through the design of distributed data-driven techniques based on the support vector machine method, where each relay is responsible for distributed data collection, fault detection, fault localization, and fault isolation. Local communication is established among neighboring relays, fostering cooperative fault localization and isolation. This decentralized design not only reduces the computational and communication requirements but also enables the adaptability of each relay under varying operational dynamics. The proposed end-to-end protection framework was validated using MATLAB/Simulink simulations on a 100% renewable microgrid, achieving an accuracy of 93.1% with response time of 0.0523 s, in protecting against a range of fault scenarios that are characterized by various types, locations, impedances, load conditions, photovoltaic power levels, and microgrid operating modes.. Authors: Chen, Yue [National Renewable Energy Laboratory (NREL), Golden, CO (United States)] (ORCID:0000000310707192); Chakraborty, Soham [National Renewable Energy Laboratory (NREL), Golden, CO (United States)]; Zamzam, Ahmed [Ascend Analytics, Boulder, CO (United States)] (ORCID:0000000211752801); Wang, Jing [National Renewable Energy Laboratory (NREL), Golden, CO (United States)]. DOE Contract: AC36-08GO28308. Subjects: 24 POWER TRANSMISSION AND DISTRIBUTION; 97 MATHEMATICS AND COMPUTING; decentralized decision; end-to-end protection; fault detection; fault isolation; fault localization; machine learning; microgrid; support vector machine