Photovoltaic Array Reconfiguration under Partial Shading Conditions Based on Short-Circuit Current Estimated by Convolutional Neural Network
Partial shading conditions (PSC) have negative effects on the operation of photovoltaic (PV) systems. In this paper, a PV array reconfiguration method is developed to minimize power losses of PV arrays under partial shading conditions. The proposed reconfiguration method is based on equalizing the r...
| Hlavní autoři: | , , , , |
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| Médium: | Článek |
| Jazyk: | English |
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MDPI AG
2022-08-01
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| Edice: | Energies |
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| On-line přístup: | https://www.mdpi.com/1996-1073/15/17/6341 |
| _version_ | 1827666435697016832 |
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| author | Tuyen Nguyen-Duc Thinh Le-Viet Duong Nguyen-Dang Tung Dao-Quang Minh Bui-Quang |
| author_facet | Tuyen Nguyen-Duc Thinh Le-Viet Duong Nguyen-Dang Tung Dao-Quang Minh Bui-Quang |
| author_sort | Tuyen Nguyen-Duc |
| collection | DOAJ |
| description | Partial shading conditions (PSC) have negative effects on the operation of photovoltaic (PV) systems. In this paper, a PV array reconfiguration method is developed to minimize power losses of PV arrays under partial shading conditions. The proposed reconfiguration method is based on equalizing the reduction of the short-circuit current of the PV modules in the PV array. Eight state-of-the-art Convolutional Neural Network models are employed to estimate the effect of shading on the short-circuit current of a PV module. These models include LeNet-5, AlexNet, VGG 11, VGG 19, Inception V3, ResNet 18, ResNet 34, and ResNet 50. Among eight models, the VGG 19 achieves the best accuracy on 1842 sample images. Therefore, this model is used to estimate the ratio of the actual short-circuit current and the estimated short-circuit current in four studied shading scenarios. This ratio decides the switching rule between PV modules throughout the PV array under PSC. A <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2</mn><mo>×</mo><mn>2</mn></mrow></semantics></math></inline-formula> experimental PV array shows that the proposed reconfiguration method improves the output power from 5.81% to 25.19% in four shading patterns. Accordingly, the power losses are reduced from 1.32% to 13.75%. The power improvement and the reduction of power losses of the proposed dynamic PV array reconfiguration system under four case studies demonstrates its effectiveness in addressing the effects of PSC on the PV array. |
| first_indexed | 2024-03-10T01:51:39Z |
| format | Article |
| id | doaj.art-15abd982cae04e6fa76589c66e0121a8 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T01:51:39Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-15abd982cae04e6fa76589c66e0121a82023-11-23T13:04:31ZengMDPI AGEnergies1996-10732022-08-011517634110.3390/en15176341Photovoltaic Array Reconfiguration under Partial Shading Conditions Based on Short-Circuit Current Estimated by Convolutional Neural NetworkTuyen Nguyen-Duc0Thinh Le-Viet1Duong Nguyen-Dang2Tung Dao-Quang3Minh Bui-Quang4Department of Electrical Engineering, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi 100000, VietnamDepartment of Electrical Engineering, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi 100000, VietnamDepartment of Electrical Engineering, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi 100000, VietnamDepartment of Electrical Engineering, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi 100000, VietnamDepartment of Electrical Engineering, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi 100000, VietnamPartial shading conditions (PSC) have negative effects on the operation of photovoltaic (PV) systems. In this paper, a PV array reconfiguration method is developed to minimize power losses of PV arrays under partial shading conditions. The proposed reconfiguration method is based on equalizing the reduction of the short-circuit current of the PV modules in the PV array. Eight state-of-the-art Convolutional Neural Network models are employed to estimate the effect of shading on the short-circuit current of a PV module. These models include LeNet-5, AlexNet, VGG 11, VGG 19, Inception V3, ResNet 18, ResNet 34, and ResNet 50. Among eight models, the VGG 19 achieves the best accuracy on 1842 sample images. Therefore, this model is used to estimate the ratio of the actual short-circuit current and the estimated short-circuit current in four studied shading scenarios. This ratio decides the switching rule between PV modules throughout the PV array under PSC. A <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2</mn><mo>×</mo><mn>2</mn></mrow></semantics></math></inline-formula> experimental PV array shows that the proposed reconfiguration method improves the output power from 5.81% to 25.19% in four shading patterns. Accordingly, the power losses are reduced from 1.32% to 13.75%. The power improvement and the reduction of power losses of the proposed dynamic PV array reconfiguration system under four case studies demonstrates its effectiveness in addressing the effects of PSC on the PV array.https://www.mdpi.com/1996-1073/15/17/6341partial shading conditionsimage processingConvolutional Neural Networkdynamic photovoltaic array reconfigurationexperimental analysis |
| spellingShingle | Tuyen Nguyen-Duc Thinh Le-Viet Duong Nguyen-Dang Tung Dao-Quang Minh Bui-Quang Photovoltaic Array Reconfiguration under Partial Shading Conditions Based on Short-Circuit Current Estimated by Convolutional Neural Network Energies partial shading conditions image processing Convolutional Neural Network dynamic photovoltaic array reconfiguration experimental analysis |
| title | Photovoltaic Array Reconfiguration under Partial Shading Conditions Based on Short-Circuit Current Estimated by Convolutional Neural Network |
| title_full | Photovoltaic Array Reconfiguration under Partial Shading Conditions Based on Short-Circuit Current Estimated by Convolutional Neural Network |
| title_fullStr | Photovoltaic Array Reconfiguration under Partial Shading Conditions Based on Short-Circuit Current Estimated by Convolutional Neural Network |
| title_full_unstemmed | Photovoltaic Array Reconfiguration under Partial Shading Conditions Based on Short-Circuit Current Estimated by Convolutional Neural Network |
| title_short | Photovoltaic Array Reconfiguration under Partial Shading Conditions Based on Short-Circuit Current Estimated by Convolutional Neural Network |
| title_sort | photovoltaic array reconfiguration under partial shading conditions based on short circuit current estimated by convolutional neural network |
| topic | partial shading conditions image processing Convolutional Neural Network dynamic photovoltaic array reconfiguration experimental analysis |
| url | https://www.mdpi.com/1996-1073/15/17/6341 |
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