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

Nasser Yousefi

doi:10.11591/ijeei.v3i4.201

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.

Highlights

The traditional distributed generation systems are wind power generation, photovoltaic power generation, fuel cell power generation, and micro-turbine power generation
It is noticed from the graphs that during the fault the details dropped to zero since the Distributed Generator (DG) is equipped with UOF/UOV protective relay which isolated the fault
A new technique for islanding detection of distributed generation is suggested on the bases of Hybrid Wavelet Neural Network with Interactive Honey Bee Matting Optimization (IHBMO)

Summary

Introduction

The traditional distributed generation systems are wind power generation, photovoltaic power generation, fuel cell power generation, and micro-turbine power generation. The other inactive procedures have been suggested on the basis of controlling rate of change of frequency (ROCOF), phase angle displacement, rate of change of generator power output, impedance monitoring, the THD producer and the wavelet transform function [23] These offer superior sensitivity as their settings allow detection to take place within statutory limits, but their settings must be attentively chosen to prevent mal-operation during network faults. In order to determine the quantity of mismatch for which the OVP/UVP and OFP/UFP will fail to detect islanding, the quantity of active power mismatch in terms of load resistance can be demonstrated as follows:. A common coupling point voltage is caused by the power grid, and distributed generation system has no control overvoltage and until it is connected to the network the voltage is unchanged at nominal value of 1 pu. Since the output power of the inverter is in unity power factor, before islanding reactive power of load is supplied just by network and after islanding the quantity of reactive power imbalance is equal to the consumed load before islanding, we have:

V2 nL

E D1a k d

Conclusion