Euclidean Distance-Based Tree Algorithm for Fault Detection and Diagnosis in Photovoltaic Systems
In this paper, a new methodology for fault detection and diagnosis in photovoltaic systems is proposed. This method employs a novel Euclidean distance-based tree algorithm to classify various considered faults. Unlike the decision tree, which requires the use of the Gini index to split the data, thi...
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2025-04-01
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| author | Youssouf Mouleloued Kamel Kara Aissa Chouder Abdelhadi Aouaichia Santiago Silvestre |
| author_facet | Youssouf Mouleloued Kamel Kara Aissa Chouder Abdelhadi Aouaichia Santiago Silvestre |
| author_sort | Youssouf Mouleloued |
| collection | DOAJ |
| description | In this paper, a new methodology for fault detection and diagnosis in photovoltaic systems is proposed. This method employs a novel Euclidean distance-based tree algorithm to classify various considered faults. Unlike the decision tree, which requires the use of the Gini index to split the data, this algorithm mainly relies on computing distances between an arbitrary point in the space and the entire dataset. Then, the minimum and the maximum distances of each class are extracted and ordered in ascending order. The proposed methodology requires four attributes: Solar irradiance, temperature, and the coordinates of the maximum power point (Impp, Vmpp). The developed procedure for fault detection and diagnosis is implemented and applied to classify a dataset comprising seven distinct classes: normal operation, string disconnection, short circuit of three modules, short circuit of ten modules, and three cases of string disconnection, with 25%, 50%, and 75% of partial shading. The obtained results demonstrate the high efficiency and effectiveness of the proposed methodology, with a classification accuracy reaching 97.33%. A comparison study between the developed fault detection and diagnosis methodology and Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbors algorithms is conducted. The proposed procedure shows high performance against the other algorithms in terms of accuracy, precision, recall, and F1-score. |
| format | Article |
| id | doaj-art-5a52ce92f9ea40599daaa59781456583 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-5a52ce92f9ea40599daaa597814565832025-08-20T02:17:00ZengMDPI AGEnergies1996-10732025-04-01187177310.3390/en18071773Euclidean Distance-Based Tree Algorithm for Fault Detection and Diagnosis in Photovoltaic SystemsYoussouf Mouleloued0Kamel Kara1Aissa Chouder2Abdelhadi Aouaichia3Santiago Silvestre4Laboratoire des Systèmes Electriques et Télécommande, Faculté de Technologie, Université Blida 1, BP 270, Blida 09000, AlgeriaLaboratoire des Systèmes Electriques et Télécommande, Faculté de Technologie, Université Blida 1, BP 270, Blida 09000, AlgeriaElectrical Engineering Laboratory (LGE), University Mohamed Boudiaf of M’sila, BP 166, M’sila 28000, AlgeriaLaboratoire des Systèmes Electriques et Télécommande, Faculté de Technologie, Université Blida 1, BP 270, Blida 09000, AlgeriaDepartment of Electronic Engineering, Universitat Politècnica de Catalunya, 08034 Barcelona, SpainIn this paper, a new methodology for fault detection and diagnosis in photovoltaic systems is proposed. This method employs a novel Euclidean distance-based tree algorithm to classify various considered faults. Unlike the decision tree, which requires the use of the Gini index to split the data, this algorithm mainly relies on computing distances between an arbitrary point in the space and the entire dataset. Then, the minimum and the maximum distances of each class are extracted and ordered in ascending order. The proposed methodology requires four attributes: Solar irradiance, temperature, and the coordinates of the maximum power point (Impp, Vmpp). The developed procedure for fault detection and diagnosis is implemented and applied to classify a dataset comprising seven distinct classes: normal operation, string disconnection, short circuit of three modules, short circuit of ten modules, and three cases of string disconnection, with 25%, 50%, and 75% of partial shading. The obtained results demonstrate the high efficiency and effectiveness of the proposed methodology, with a classification accuracy reaching 97.33%. A comparison study between the developed fault detection and diagnosis methodology and Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbors algorithms is conducted. The proposed procedure shows high performance against the other algorithms in terms of accuracy, precision, recall, and F1-score.https://www.mdpi.com/1996-1073/18/7/1773fault detection and diagnosisFDDsupervision algorithmbinary classificationshort circuitspartial shading |
| spellingShingle | Youssouf Mouleloued Kamel Kara Aissa Chouder Abdelhadi Aouaichia Santiago Silvestre Euclidean Distance-Based Tree Algorithm for Fault Detection and Diagnosis in Photovoltaic Systems Energies fault detection and diagnosis FDD supervision algorithm binary classification short circuits partial shading |
| title | Euclidean Distance-Based Tree Algorithm for Fault Detection and Diagnosis in Photovoltaic Systems |
| title_full | Euclidean Distance-Based Tree Algorithm for Fault Detection and Diagnosis in Photovoltaic Systems |
| title_fullStr | Euclidean Distance-Based Tree Algorithm for Fault Detection and Diagnosis in Photovoltaic Systems |
| title_full_unstemmed | Euclidean Distance-Based Tree Algorithm for Fault Detection and Diagnosis in Photovoltaic Systems |
| title_short | Euclidean Distance-Based Tree Algorithm for Fault Detection and Diagnosis in Photovoltaic Systems |
| title_sort | euclidean distance based tree algorithm for fault detection and diagnosis in photovoltaic systems |
| topic | fault detection and diagnosis FDD supervision algorithm binary classification short circuits partial shading |
| url | https://www.mdpi.com/1996-1073/18/7/1773 |
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