Real-Time Adaptive Differential Feature-Based Protection Scheme for Isolated Microgrids Using Edge Computing

Inhabited isolated areas require independent power systems called isolated microgrids. Conventional protection schemes are not suitable for isolated microgrids due to their dependence on significant fault current. This article introduces an adaptive differential feature-based protection scheme for fault detection and faulty phase identification for isolated microgrids. Instead of differential currents, it proposes differential features, where edge computing is used to extract the features at point of measurement. The most informative one-dimensional feature is extracted from each phase at both ends of the line. The line differential feature is compared with an adaptive threshold to perform fault detection. The threshold is updated every second and is immune to system noises. Faulty phase identification is performed with a logical comparison. An in-memory circular buffer is proposed to store the local features in case of communication delay. Communication and synchronization are experimentally validated, and the worst-case latency is reported along with individual processing time for data acquisition, feature extraction, communication, and fault detection. Extensive validations are done with IEC-based and IEEE 13-node-based isolated microgrid. The test results are compared with state-of-the-art methods and proved that the proposed scheme is practical to be adopted in real- world isolated microgrids of different sizes and topologies.