DOI:10.35833/MPCE.2019.000255 |
| |
| |
Intelligent Islanding Detection of Multi-distributed Generation Using Artificial Neural Network Based on Intrinsic Mode Function Feature |
| |
|
| |
Page view: 115
Net amount: 645 |
| |
Author:
Admasie Samuel1,Bukhari Syed Basit Ali1,2,Gush Teke1,Haider Raza1,3,Kim Chul Hwan1
|
Author Affiliation:
1.Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea, South Korea;2.U.S.-Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), H-12 Islamabad, Pakistan;3.Department of Electrical Engineering, Balochistan University of Engineering and Technology, Khuzdar, 89100, Pakistan
|
Foundation: |
This work was supported by the National Research Foundation (NRF) of South Korea funded by the Ministry of Science, ICT & Future Planning (MSIP) of the Korean government (No. 2018R1A2A1A05078680). |
|
|
Abstract: |
The integration of distributed energy resources (DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants, and storage systems. Nevertheless, inadvertent islanding operation is one of the major protection issues in distribution networks connected to DERs. This study proposes an intelligent islanding detection method (IIDM) using an intrinsic mode function (IMF) feature-based grey wolf optimized artificial neural network (GWO-ANN). In the proposed IIDM, the modal voltage signal is pre-processed by variational mode decomposition followed by Hilbert transform on each IMF to derive highly involved features. Then, the energy and standard deviation of IMFs are employed to train/test the GWO-ANN model for identifying the islanding operations from other non-islanding events. To evaluate the performance of the proposed IIDM, various islanding and non-islanding conditions such as faults, voltage sag, linear and nonlinear load and switching, are considered as the training and testing datasets. Moreover, the proposed IIDM is evaluated under noise conditions for the measured voltage signal. The simulation results demonstrate that the proposed IIDM is capable of differentiating between islanding and non-islanding events without any sensitivity under noise conditions in the test signal. |
Keywords: |
Distributed energy resource (DER) ; intrinsic mode function (IMF) ; grey wolf optimized artificial neural network (GWO-ANN) ; intelligent islanding detection method (IIDM) ; microgrid |
| |
Received:April 16, 2019
Online Time:2020/06/03 |
| |
|
|
View Full Text
Download reader
|
|
|