A New Hybrid Wavelet Neural Network and Interactive Honey Bee Matting Optimization Based on Islanding Detection

Abstract

In this paper a passive Neuro-wavelet on the basis of islanding detection procedure for grid-connected inverter-based distributed generation has been developed. Moreover, the weight parameters of neural network are optimized by Interactive Honey Bee Matting optimization (IHBMO) to increase the efficiency of the capability of suggested procedure in tendered problem. Islanding is the situation where the distribution system including both distributed generator and loads is disconnected from the major grid as a consequence of lots of reasons such as electrical faults and their subsequent switching incidents, equipment failure, or pre-planned switching events like maintenance. The suggested method uses and combines wavelet analysis and artificial neural network together to detect islanding. It can be used in removing discriminative characteristics from the acquired voltage signals. In passive schemes have a large Non Detection Zone (NDZ), concern has been raised on active method because of its lowering power quality impact. The main focus of the proposed scheme is to decrease the NDZ to as close as possible and to retain the output power quality fixed. The simulations results, performed by MATLAB/Simulink, demonstrate that the mentioned procedure has a small non-detection zone. What is more, this method is capable of detecting islanding precisely within the least possible amount of standard time.