A Novel ANFIS-Based Islanding Detection for Inverter-Interfaced Microgrids

This paper presents a new islanding detection strategy for low-voltage inverter-interfaced microgrids based on adaptive neuro-fuzzy inference system (ANFIS). The proposed islanding detection method exploits the pattern recognition capability of ANFIS and its nonlinear mapping of relation between inputs. The ANFIS monitors seven inputs measured at point of common coupling (PCC), namely root-mean square (RMS) of voltage and current (RMSU and RMSI), total harmonic distortion (THD) of voltage and current (THDU and THDI), frequency (f), and active and reactive powers (P, Q) that are experimentally obtained based on practical measurement in a real-life microgrid. The proposed method is composed of passive monitoring of the mentioned inputs. It does not influence power quality (PQ) and considerably decreases non detection zones (NDZs). In order to cover as many situations as possible, minimize false tripping and still remain selective; type and number of samples are introduced. Here, one of the primary goals is reducing NDZ while keeping PQ in order. Based on the sampled frequency and number of samples, we find that the proposed method has less detection time and better accuracy when compared to the reported methods. Simulations performed in MATLAB/Simulink software environment and several tests performed based on different active load conditions and multiple distributed generation, prove the effectiveness, authenticity, selectivity, accuracy and precision of the proposed method with allowable impact on PQ according to UL1741 standard.