Islanding Detection Scheme of Distributed Generation Systems using Hybrid FAT-SGO Approach
Sathish K R1, T Ananthapadmanabha2

1Sathish K R, Assistant Professor, Department of EEE, ATMECE, Mysuru, India.
1Dr. T Ananthapadmanabha, Former Principal of NIEIT, Mysuru, India. 

Manuscript received on September 21, 2020. | Revised Manuscript received on November 01, 2020. | Manuscript published on November 10, 2021. | PP: 213-218 | Volume-10 Issue-1, November 2020 | Retrieval Number: 100.1/ijitee.A81651110120| DOI: 10.35940/ijitee.A8165.1110120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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. 
Keywords: Distributed generation, Islanding detection, Feedback Artificial Tree algorithm, Shell Game Optimization, Non‐detection zone.