Photovoltaic Array Reconfiguration under Partial Shading Conditions Based on Short-Circuit Current Estimated by Convolutional Neural Network

Tuyen Nguyen-Duc (), Thinh Le-Viet, Duong Nguyen-Dang, Tung Dao-Quang and Minh Bui-Quang
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Tuyen Nguyen-Duc: Department of Electrical Engineering, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam
Thinh Le-Viet: Department of Electrical Engineering, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam
Duong Nguyen-Dang: Department of Electrical Engineering, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam
Tung Dao-Quang: Department of Electrical Engineering, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam
Minh Bui-Quang: Department of Electrical Engineering, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam

Energies, 2022, vol. 15, issue 17, 1-21

Abstract: 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 2 × 2 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.

Keywords: partial shading conditions; image processing; Convolutional Neural Network; dynamic photovoltaic array reconfiguration; experimental analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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