An islanding detection method for grid-connect inverter based on parameter optimized variational mode decomposition and deep learning
The rapid and effective islanding detection and disconnection of the microgrid are significant for preventing equipment from failure and safeguarding humanity’s safety. To address the drawbacks of active methods and passive methods, an intelligent islanding detection strategy based on parameter-opti...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2025.1445522/full |
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| _version_ | 1850140500382711808 |
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| author | Yan Xia Yan Xia Yuli Lv Feihong Yu Yiqiang Yang Yili Yang Wei Li Ke Li |
| author_facet | Yan Xia Yan Xia Yuli Lv Feihong Yu Yiqiang Yang Yili Yang Wei Li Ke Li |
| author_sort | Yan Xia |
| collection | DOAJ |
| description | The rapid and effective islanding detection and disconnection of the microgrid are significant for preventing equipment from failure and safeguarding humanity’s safety. To address the drawbacks of active methods and passive methods, an intelligent islanding detection strategy based on parameter-optimized variational mode decomposition (VMD) and deep learning was developed. Firstly, the proposed adaptive variational mode decomposition (AVMD) strategy improves the optimal mode number and penalty term of VMD by utilizing the relative entropy between the original signal and the intrinsic mode functions (IMFs). Then, the Teager energy operator (TEO) further extracts sequence features to track the instantaneous energy of the IMFs. Finally, the AVMD-TEO-MPE -based features are used to train the one-dimensional convolutional neural network (1D-CNN) as a deep learning classifier. Simulation results indicate that the proposed method can effectively differentiate the islanding state under different working conditions with a testing accuracy level of 100% within a maximal detection time of 46.402 ms. It is also noise resistant to a degree. Comparative analysis confirms that the proposed method outperforms the existing method in distinguishing between islanding and non-islanding events. |
| format | Article |
| id | doaj-art-95589bba1ddc4ca1a8ef532bd35f028a |
| institution | OA Journals |
| issn | 2296-598X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj-art-95589bba1ddc4ca1a8ef532bd35f028a2025-08-20T02:29:46ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2025-04-011310.3389/fenrg.2025.14455221445522An islanding detection method for grid-connect inverter based on parameter optimized variational mode decomposition and deep learningYan Xia0Yan Xia1Yuli Lv2Feihong Yu3Yiqiang Yang4Yili Yang5Wei Li6Ke Li7School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, ChinaKey Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things, Yibin, ChinaSchool of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, ChinaZigong Branch of China Telecom Co., Ltd., Zigong, ChinaSchool of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, ChinaZonergy Co., Ltd., Zigong, ChinaZonergy Co., Ltd., Zigong, ChinaHydrogen Energy and Multi-Energy Complementary Microgrid Engineering Technology Research Center of Sichuan Province, Mianyang, ChinaThe rapid and effective islanding detection and disconnection of the microgrid are significant for preventing equipment from failure and safeguarding humanity’s safety. To address the drawbacks of active methods and passive methods, an intelligent islanding detection strategy based on parameter-optimized variational mode decomposition (VMD) and deep learning was developed. Firstly, the proposed adaptive variational mode decomposition (AVMD) strategy improves the optimal mode number and penalty term of VMD by utilizing the relative entropy between the original signal and the intrinsic mode functions (IMFs). Then, the Teager energy operator (TEO) further extracts sequence features to track the instantaneous energy of the IMFs. Finally, the AVMD-TEO-MPE -based features are used to train the one-dimensional convolutional neural network (1D-CNN) as a deep learning classifier. Simulation results indicate that the proposed method can effectively differentiate the islanding state under different working conditions with a testing accuracy level of 100% within a maximal detection time of 46.402 ms. It is also noise resistant to a degree. Comparative analysis confirms that the proposed method outperforms the existing method in distinguishing between islanding and non-islanding events.https://www.frontiersin.org/articles/10.3389/fenrg.2025.1445522/fullislanding detectionrelative entropyvariational mode decompositionteager energy operatormulti-scale permutation entropyconvolutional neural network |
| spellingShingle | Yan Xia Yan Xia Yuli Lv Feihong Yu Yiqiang Yang Yili Yang Wei Li Ke Li An islanding detection method for grid-connect inverter based on parameter optimized variational mode decomposition and deep learning Frontiers in Energy Research islanding detection relative entropy variational mode decomposition teager energy operator multi-scale permutation entropy convolutional neural network |
| title | An islanding detection method for grid-connect inverter based on parameter optimized variational mode decomposition and deep learning |
| title_full | An islanding detection method for grid-connect inverter based on parameter optimized variational mode decomposition and deep learning |
| title_fullStr | An islanding detection method for grid-connect inverter based on parameter optimized variational mode decomposition and deep learning |
| title_full_unstemmed | An islanding detection method for grid-connect inverter based on parameter optimized variational mode decomposition and deep learning |
| title_short | An islanding detection method for grid-connect inverter based on parameter optimized variational mode decomposition and deep learning |
| title_sort | islanding detection method for grid connect inverter based on parameter optimized variational mode decomposition and deep learning |
| topic | islanding detection relative entropy variational mode decomposition teager energy operator multi-scale permutation entropy convolutional neural network |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2025.1445522/full |
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