engleski

A Novel ANFIS-based Islanding Detection for Inverter–Interfaced Microgrids

This paper presents a new islanding detection strategyfor low-voltage (LV) 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 voltageand current (RMSUand RMSI), total harmonic distortion (THD) of voltage and current (THDUand THDI), frequency (f), and also 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 and therefore, does not influence power quality (PQ) ; but considerably decreases non detection zones (NDZs). In order to cover as much situations as possible, minimizing false tripping and still remaining selective, type and number of samples areintroduced. Here, one of the main goals is reducing NDZ by still keeping PQ in order. Based on the sampled frequency and number of samples, we find that the proposed method hasless detection time and better accuracy, compared to the reported methods. S imulations performedin MATLAB/Simulink softwareenvironment and several tests performed based on different active load conditions and multiple distributed generation (DG)s, prove the effectiveness, authenticity, selectivity, accuracy and precision of the proposed method with allowable impact on PQ according to UL1741 standard.

Adaptive neuro-fuzzy inference system ; distributed generation ; inverter-interfaced microgrid ; islanding detection, power quality ; total harmonic distortion

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