Statistical Analysis and Development of an Ensemble-Based Machine Learning Model for Photovoltaic Fault Detection
This paper presents a framework for photovoltaic (PV) fault detection based on statistical, supervised, and unsupervised machine learning (ML) approaches. The research is motivated by a need to develop a cost-effective solution that detects the fault types within PV systems based on a real dataset w...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/15/5492 |
| _version_ | 1797414130671419392 |
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| author | Muhammad Hussain Hussain Al-Aqrabi Richard Hill |
| author_facet | Muhammad Hussain Hussain Al-Aqrabi Richard Hill |
| author_sort | Muhammad Hussain |
| collection | DOAJ |
| description | This paper presents a framework for photovoltaic (PV) fault detection based on statistical, supervised, and unsupervised machine learning (ML) approaches. The research is motivated by a need to develop a cost-effective solution that detects the fault types within PV systems based on a real dataset with a minimum number of input features. We discover the appropriate conditions for method selection and establish how to minimize computational demand from different ML approaches. Subsequently, the PV dataset is labeled as a result of clustering and classification. The labelled dataset is then trained using various ML models before evaluating each based on accuracy, precision, and a confusion matrix. Notably, an accuracy ranging from 94% to 100% is achieved with datasets from two different PV systems. The model robustness is affirmed by performing the approach on an additional real-world dataset that exhibits noise and missing values. |
| first_indexed | 2024-03-09T05:27:44Z |
| format | Article |
| id | doaj.art-ce431e265aab49c3b47dc35459f7dcaa |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T05:27:44Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ce431e265aab49c3b47dc35459f7dcaa2023-12-03T12:35:27ZengMDPI AGEnergies1996-10732022-07-011515549210.3390/en15155492Statistical Analysis and Development of an Ensemble-Based Machine Learning Model for Photovoltaic Fault DetectionMuhammad Hussain0Hussain Al-Aqrabi1Richard Hill2Centre for Industrial Analytics, Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UKCentre for Industrial Analytics, Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UKCentre for Industrial Analytics, Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UKThis paper presents a framework for photovoltaic (PV) fault detection based on statistical, supervised, and unsupervised machine learning (ML) approaches. The research is motivated by a need to develop a cost-effective solution that detects the fault types within PV systems based on a real dataset with a minimum number of input features. We discover the appropriate conditions for method selection and establish how to minimize computational demand from different ML approaches. Subsequently, the PV dataset is labeled as a result of clustering and classification. The labelled dataset is then trained using various ML models before evaluating each based on accuracy, precision, and a confusion matrix. Notably, an accuracy ranging from 94% to 100% is achieved with datasets from two different PV systems. The model robustness is affirmed by performing the approach on an additional real-world dataset that exhibits noise and missing values.https://www.mdpi.com/1996-1073/15/15/5492photovoltaicshierarchical clusteringunsupervised learning |
| spellingShingle | Muhammad Hussain Hussain Al-Aqrabi Richard Hill Statistical Analysis and Development of an Ensemble-Based Machine Learning Model for Photovoltaic Fault Detection Energies photovoltaics hierarchical clustering unsupervised learning |
| title | Statistical Analysis and Development of an Ensemble-Based Machine Learning Model for Photovoltaic Fault Detection |
| title_full | Statistical Analysis and Development of an Ensemble-Based Machine Learning Model for Photovoltaic Fault Detection |
| title_fullStr | Statistical Analysis and Development of an Ensemble-Based Machine Learning Model for Photovoltaic Fault Detection |
| title_full_unstemmed | Statistical Analysis and Development of an Ensemble-Based Machine Learning Model for Photovoltaic Fault Detection |
| title_short | Statistical Analysis and Development of an Ensemble-Based Machine Learning Model for Photovoltaic Fault Detection |
| title_sort | statistical analysis and development of an ensemble based machine learning model for photovoltaic fault detection |
| topic | photovoltaics hierarchical clustering unsupervised learning |
| url | https://www.mdpi.com/1996-1073/15/15/5492 |
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