Zero NDZ assessment for anti-islanding protection using wavelet analysis and neuro-fuzzy system in inverter based distributed generation

Due to increase of electrical power demand, several uncommon sources mainly voltage source converter (VSC) based distributed generations (DGs) have been included into the power systems which increased the systems complexity and uncertainty. One of the most problem of DGs is unwanted islanding. This paper addresses a reliable passive time–frequency islanding detection algorithm using the multi signal analysis method. In addition, Adaptive Neuro Fuzzy Learning System (ANFIS) is used for decision making mechanism to avoid of threshold. Reduction of non detection zone (NDZ) is another contribution of this study. At first, all possible linear and nonlinear load switching, motor starting, capacitor bank switching, and islanding conditions are simulated and the required detection parameters measured. Using the discrete wavelet theory, the energy of any decomposition level of all mother wavelet for parameters detection is calculated. From of these signals, the best of them are selected for ANFIS training for islanding detection purpose. Simulation results confirm the performance of the proposed detection algorithm in comparison with existing methods.