Voting-Based Ensemble Learning Algorithm for Fault Detection in Photovoltaic Systems under Different Weather Conditions

Nien-Che Yang and Harun Ismail
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Nien-Che Yang: Department of Electrical Engineering, National Taiwan University of Science and Technology, No. 43, Keelung Road, Section 4, Taipei 10607, Taiwan
Harun Ismail: Department of Electrical Engineering, National Taiwan University of Science and Technology, No. 43, Keelung Road, Section 4, Taipei 10607, Taiwan

Mathematics, 2022, vol. 10, issue 2, 1-18

Abstract: A photovoltaic (PV) system is one of the renewable energy resources that can help in meeting the ever-increasing energy demand. However, installation of PV systems is prone to faults that can occur unpredictably and remain challenging to detect. Major PV faults that can occur are line-line and open circuits faults, and if they are not addressed appropriately and timely, they may lead to serious problems in the PV system. To solve this problem, this study proposes a voting-based ensemble learning algorithm with linear regression, decision tree, and support vector machine (EL-V LR-DT-SVM ) for PV fault detection and diagnosis. The data acquisition is performed for different weather conditions to trigger the nonlinear nature of the PV system characteristics. The voltage-current characteristics are used as input data. The dataset is studied for a deeper understanding, and pre-processing before feeding it to the EL-V LR-DT-SVM . In the pre-processing step, data are normalized to obtain more feature space, making it easy for the proposed algorithm to discriminate between healthy and faulty conditions. To verify the proposed method, it is compared with other algorithms in terms of accuracy, precision, recall, and F-1 score. The results show that the proposed EL-V LR-DT-SVM algorithm outperforms the other algorithms.

Keywords: voting-based ensemble learning; decision tree; linear regression; line-line fault; open circuit; PV fault detection and diagnosis; support vector machine (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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