Islanding Detection in Microgrids including VSC-based Renewable/Distributed Energy Resources: An AI-based Technique

In this paper, a new islanding detection strategy is presented for low-voltage (LV) microgrid consisting of voltage-sourced converter (VSC)-based including renewable/distributed energy resources (RERs/DERs). The nonlinear mapping of relation between inputs and pattern recognition capability of adaptive neuro-fuzzy inference system (ANFIS) is the key concept of this strategy. By measuring point of common coupling (PCC), seven signals including, root-mean square (RMS) of voltage (RMS(V)), current (RMS(I)), and total harmonic distortion (THD) of voltage (THD(V) and current (THD(I)), frequency (f), and also active and reactive powers (P, Q) are passively monitored thus, while this method reduces non detection zones (NDZs), it does not a power quality. Simulations performed in MATLAB/SIMULINK environment proves the effectiveness, authenticity, selectivity, accuracy and precision of the proposed method with allowable impact on PQ according to UL1741 standard.