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A predictive machine learning approach for early fault identification in solar photovoltaic systems.

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Title: A predictive machine learning approach for early fault identification in solar photovoltaic systems.
Authors: EL-Rashidy, Nora1 (AUTHOR), Ali, Zainab H.2,3 (AUTHOR), Sultan, Yara A.1,4 (AUTHOR) yaraabdalla2@gmail.com
Source: Electric Power Systems Research. Oct2025, Vol. 247, pN.PAG-N.PAG. 1p.
Subjects: Clean energy, Detection algorithms, Multi-objective optimization, Solar energy, Energy infrastructure, Feature selection
Abstract: • Proposed Predictive Modeling Framework : Proposes a novel framework to enhance fault detection in photovoltaic (PV) systems by integrating single-objective and multi-objective feature selection techniques. • Hybrid Feature Selection Approach : Combines Information Gain (IG), Gini Ratio (GR) , and Gini Importance (GI) for single-objective feature selection with NSGA-II for multi-objective optimization to derive an optimal feature subset. • Enhanced Fault Detection Accuracy : Aims to improve diagnostic precision in identifying and estimating potential faults within PV string systems. • Computational Efficiency Optimization : The hybrid feature selection method reduces dimensionality, improving model performance while maintaining computational efficiency. • Validation on a Real-World Dataset : Evaluates the proposed framework using a dataset of 600 instances to ensure robustness and reliability in fault detection. The reliability of photovoltaic (PV) panels plays a pivotal role in optimizing energy output, particularly as solar energy becomes an indispensable pillar in global initiatives aimed at achieving sustainable and decarbonized energy infrastructures. Since faults in PV panels can significantly impair system performance and reduce energy yield, early detection is essential to maintaining operational efficiency. This study proposes an advanced fault detection framework leveraging contemporary machine learning techniques to identify anomalies early, thereby preventing performance degradation and minimizing downtime. The proposed model incorporates single- and multi-objective feature selection strategies to enhance fault identification accuracy. Specifically, single-objective techniques utilize Information Gain (IG), Gini Ratio (GR), and Gini Importance (GI). At the same time, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed as a multi-objective method to select an optimal subset of fault-relevant features. The model was trained on a dataset comprising 600 instances. Results indicate that gradient boosting achieved 97.6 % accuracy using single-objective selection, whereas the NSGA-II-based approach reduced the feature set to 15 optimal variables and improved accuracy to 99.3 %. Implementing this model enhances the operational reliability of solar PV systems and supports the broader goal of advancing the sustainability of solar energy production. [Display omitted] [ABSTRACT FROM AUTHOR]
Copyright of Electric Power Systems Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 185872268
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: A predictive machine learning approach for early fault identification in solar photovoltaic systems.
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  Data: <searchLink fieldCode="AR" term="%22EL-Rashidy%2C+Nora%22">EL-Rashidy, Nora</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ali%2C+Zainab+H%2E%22">Ali, Zainab H.</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sultan%2C+Yara+A%2E%22">Sultan, Yara A.</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<i> yaraabdalla2@gmail.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Electric+Power+Systems+Research%22">Electric Power Systems Research</searchLink>. Oct2025, Vol. 247, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Clean+energy%22">Clean energy</searchLink><br /><searchLink fieldCode="DE" term="%22Detection+algorithms%22">Detection algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Solar+energy%22">Solar energy</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+infrastructure%22">Energy infrastructure</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: • Proposed Predictive Modeling Framework : Proposes a novel framework to enhance fault detection in photovoltaic (PV) systems by integrating single-objective and multi-objective feature selection techniques. • Hybrid Feature Selection Approach : Combines Information Gain (IG), Gini Ratio (GR) , and Gini Importance (GI) for single-objective feature selection with NSGA-II for multi-objective optimization to derive an optimal feature subset. • Enhanced Fault Detection Accuracy : Aims to improve diagnostic precision in identifying and estimating potential faults within PV string systems. • Computational Efficiency Optimization : The hybrid feature selection method reduces dimensionality, improving model performance while maintaining computational efficiency. • Validation on a Real-World Dataset : Evaluates the proposed framework using a dataset of 600 instances to ensure robustness and reliability in fault detection. The reliability of photovoltaic (PV) panels plays a pivotal role in optimizing energy output, particularly as solar energy becomes an indispensable pillar in global initiatives aimed at achieving sustainable and decarbonized energy infrastructures. Since faults in PV panels can significantly impair system performance and reduce energy yield, early detection is essential to maintaining operational efficiency. This study proposes an advanced fault detection framework leveraging contemporary machine learning techniques to identify anomalies early, thereby preventing performance degradation and minimizing downtime. The proposed model incorporates single- and multi-objective feature selection strategies to enhance fault identification accuracy. Specifically, single-objective techniques utilize Information Gain (IG), Gini Ratio (GR), and Gini Importance (GI). At the same time, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed as a multi-objective method to select an optimal subset of fault-relevant features. The model was trained on a dataset comprising 600 instances. Results indicate that gradient boosting achieved 97.6 % accuracy using single-objective selection, whereas the NSGA-II-based approach reduced the feature set to 15 optimal variables and improved accuracy to 99.3 %. Implementing this model enhances the operational reliability of solar PV systems and supports the broader goal of advancing the sustainability of solar energy production. [Display omitted] [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Electric Power Systems Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.epsr.2025.111856
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Clean energy
        Type: general
      – SubjectFull: Detection algorithms
        Type: general
      – SubjectFull: Multi-objective optimization
        Type: general
      – SubjectFull: Solar energy
        Type: general
      – SubjectFull: Energy infrastructure
        Type: general
      – SubjectFull: Feature selection
        Type: general
    Titles:
      – TitleFull: A predictive machine learning approach for early fault identification in solar photovoltaic systems.
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            NameFull: EL-Rashidy, Nora
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            NameFull: Ali, Zainab H.
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            NameFull: Sultan, Yara A.
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            – D: 01
              M: 10
              Text: Oct2025
              Type: published
              Y: 2025
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