A Hybrid Approach for Microgrid Protection System Based on Neural Network and Fuzzy Logic

With the increased penetration of distributed generations (DGs) for local generation in the microgrids, it brings about a change in the fault current level of the system and causes different challenges in the conventional protection systems. This necessitates an effective adaptive protection strategy that can capture the changes in the dynamic structure of microgrid and takes effective decisions according to the changes in the network conditions. In view of that, in this paper, to adaptively and optimally adjust protection settings consistent with the changes in the network's scenarios for prior and subsequent to fault occurrence periods, the Adaptive Neuro-Fuzzy Interfaced System (ANFIS) strategy is employed. In the proposed system there is sufficient intelligence for every protection relay to intelligently adapt protection settings. The simulation case studies verify that, the developed intelligent structure carefully tracks the microgrid changeable condition and shows acceptable performance for different conditions.