In this paper an effective hybrid FAT-SGO approach
is proposed for islanding detection of distributed generation (DG)
system. The proposed approach is the joint implementation of
Feedback Artificial Tree (FAT) and Shell Game Optimization
(SGO) named as FAT-SGO technique. Reducing the
non-detection zone (NDZ) as near as possible and keep the output
power quality unmovable is main contribution of this paper.
Furthermore, this method solves the issue of establishing
detection thresholds inherent in existing methods. The proposed
strategy uses the rate of change of frequency (ROCOF) in DG
destination location is utilized as input sets of FAT system for
intelligent islanding detection. Here, FAT is trained by SGO,
which extracts the different intrinsic characteristics among
islanding and grid disturbance. With the extracted
characteristics, the FAT method is used for classifying the
disturbances in islanding and grid. For authenticating the
feasibility of this strategy is authorized through various
conditions and different conditions of load, switching operation,
and network. The simulation of the proposal is done in MATLAB
/ SIMULINK and the performance in islanding and
non-islanding events was studied. Statistic analysis of proposed
and existing methods of mean, median and standard deviation is
analyzed. DG performance is assessed by comparative analysis
with current techniques.
An Analytical Survey of the Islanding Detection Techniques of Distributed Generation Systems
