An Intelligent Deep Convolutional Neural Networks-Based Islanding Detection for Multi-DG Systems
Unintentional islanding is a problem in electrical distribution networks; it happens when the central utility is unintentionally separated from the rest of the distributed power system. The islanding detection problem becomes severe in non-detection zones. We propose an intelligent islanding detecti...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9989346/ |
| _version_ | 1797978275039936512 |
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| author | Arif Hussain Chul-Hwan Kim Muhammad Shahid Jabbar |
| author_facet | Arif Hussain Chul-Hwan Kim Muhammad Shahid Jabbar |
| author_sort | Arif Hussain |
| collection | DOAJ |
| description | Unintentional islanding is a problem in electrical distribution networks; it happens when the central utility is unintentionally separated from the rest of the distributed power system. The islanding detection problem becomes severe in non-detection zones. We propose an intelligent islanding detection technique with zero non-detection zone for a hybrid distributed generation system. It is based on the computation of frequency spectrum variations over time using short-term Fourier transform and convolutional neural networks. For various islanding and non-islanding occurrences, the three-phase voltage at the point of common coupling is monitored, and time-series data is collected. Then computations for a set of multiple frequencies on scaled time-series data are carried out, and complex numbers are split into magnitude and phase values. To detect islanding and non-islanding occurrences, a modified convolutional neural network with forward propagation was utilized. For the IEC 61850-7-420 test system, several islanding and non-islanding scenarios are created and deployed to train the convolutional neural network for the proposed approach. The efficacy of the proposed islanding detection learning model is assessed using 5-fold cross-validation. The findings reveal that under normal and noisy conditions, the proposed methodology has zero non-detection zone with original dataset, excellent accuracy, selectivity, and sensitivity. |
| first_indexed | 2024-04-11T05:21:30Z |
| format | Article |
| id | doaj.art-3c0a0827b5e84e6c91383311a1b99566 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T05:21:30Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3c0a0827b5e84e6c91383311a1b995662022-12-24T00:00:30ZengIEEEIEEE Access2169-35362022-01-011013192013193110.1109/ACCESS.2022.32296989989346An Intelligent Deep Convolutional Neural Networks-Based Islanding Detection for Multi-DG SystemsArif Hussain0https://orcid.org/0000-0002-6573-457XChul-Hwan Kim1https://orcid.org/0000-0002-3418-9687Muhammad Shahid Jabbar2https://orcid.org/0000-0003-2331-876XDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South KoreaDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South KoreaDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South KoreaUnintentional islanding is a problem in electrical distribution networks; it happens when the central utility is unintentionally separated from the rest of the distributed power system. The islanding detection problem becomes severe in non-detection zones. We propose an intelligent islanding detection technique with zero non-detection zone for a hybrid distributed generation system. It is based on the computation of frequency spectrum variations over time using short-term Fourier transform and convolutional neural networks. For various islanding and non-islanding occurrences, the three-phase voltage at the point of common coupling is monitored, and time-series data is collected. Then computations for a set of multiple frequencies on scaled time-series data are carried out, and complex numbers are split into magnitude and phase values. To detect islanding and non-islanding occurrences, a modified convolutional neural network with forward propagation was utilized. For the IEC 61850-7-420 test system, several islanding and non-islanding scenarios are created and deployed to train the convolutional neural network for the proposed approach. The efficacy of the proposed islanding detection learning model is assessed using 5-fold cross-validation. The findings reveal that under normal and noisy conditions, the proposed methodology has zero non-detection zone with original dataset, excellent accuracy, selectivity, and sensitivity.https://ieeexplore.ieee.org/document/9989346/Convolution neural networks (CNN)distributed generation (DG)islanding detectionnon-detection zone (NDZ)short-term-Fourier-transform (STFT) |
| spellingShingle | Arif Hussain Chul-Hwan Kim Muhammad Shahid Jabbar An Intelligent Deep Convolutional Neural Networks-Based Islanding Detection for Multi-DG Systems IEEE Access Convolution neural networks (CNN) distributed generation (DG) islanding detection non-detection zone (NDZ) short-term-Fourier-transform (STFT) |
| title | An Intelligent Deep Convolutional Neural Networks-Based Islanding Detection for Multi-DG Systems |
| title_full | An Intelligent Deep Convolutional Neural Networks-Based Islanding Detection for Multi-DG Systems |
| title_fullStr | An Intelligent Deep Convolutional Neural Networks-Based Islanding Detection for Multi-DG Systems |
| title_full_unstemmed | An Intelligent Deep Convolutional Neural Networks-Based Islanding Detection for Multi-DG Systems |
| title_short | An Intelligent Deep Convolutional Neural Networks-Based Islanding Detection for Multi-DG Systems |
| title_sort | intelligent deep convolutional neural networks based islanding detection for multi dg systems |
| topic | Convolution neural networks (CNN) distributed generation (DG) islanding detection non-detection zone (NDZ) short-term-Fourier-transform (STFT) |
| url | https://ieeexplore.ieee.org/document/9989346/ |
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