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Predictive Maintenance in PV Systems: A Copula-Based Approach with Digital Twin Technique.

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Title: Predictive Maintenance in PV Systems: A Copula-Based Approach with Digital Twin Technique.
Authors: Zhang, Songjie1 (AUTHOR), Yang, Xinyi1,2 (AUTHOR), Qi, Donglian1,2,3 (AUTHOR) qidl@zju.edu.cn, Xu, Zhao1,3 (AUTHOR), Wang, Minghao2,3 (AUTHOR), Yan, Yunfeng1,3 (AUTHOR)
Source: Energies (19961073). Jun2026, Vol. 19 Issue 11, p2686. 18p.
Subject Terms: *Digital twin, *Copula functions, *Photovoltaic power systems, *Statistical correlation, *Renewable energy sources, *Machine learning, *Outlier detection, *Condition-based maintenance
Abstract: Currently, solar photovoltaic (PV) systems are a priority for end-use decarbonization, aimed at reducing reliance on fossil fuels. However, PV systems are typically exposed to outdoor conditions, making them more susceptible to aging and damage. In this paper, a predictive maintenance approach that integrates digital twin technology with the copula-based model is proposed. This integration enables accurate simulation of the PV system's condition and precise representation of the correlation between the power output of the digital twin and that of the actual system. Given the power output of the digital twin, predictive maintenance is performed based on the conditional cumulative distribution function (CDF) of the actual power output, which is derived from the copula model. A comprehensive case study was conducted to evaluate the performance of the proposed approach named OCAD (Optimal Copula-based Anomaly Detector), which achieved an accuracy of 92.51% and an F1-score of 92.13%. This significantly outperforms conventional models, including SVM, KNN, and ANN, demonstrating the effectiveness of the proposed predictive maintenance strategy. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194588074
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
  Group: Ti
  Data: Predictive Maintenance in PV Systems: A Copula-Based Approach with Digital Twin Technique.
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Songjie%22">Zhang, Songjie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Xinyi%22">Yang, Xinyi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qi%2C+Donglian%22">Qi, Donglian</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> qidl@zju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xu%2C+Zhao%22">Xu, Zhao</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Minghao%22">Wang, Minghao</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yan%2C+Yunfeng%22">Yan, Yunfeng</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 11, p2686. 18p.
– Name: Subject
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  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Digital+twin%22">Digital twin</searchLink><br />*<searchLink fieldCode="DE" term="%22Copula+functions%22">Copula functions</searchLink><br />*<searchLink fieldCode="DE" term="%22Photovoltaic+power+systems%22">Photovoltaic power systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink><br />*<searchLink fieldCode="DE" term="%22Renewable+energy+sources%22">Renewable energy sources</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink><br />*<searchLink fieldCode="DE" term="%22Condition-based+maintenance%22">Condition-based maintenance</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Currently, solar photovoltaic (PV) systems are a priority for end-use decarbonization, aimed at reducing reliance on fossil fuels. However, PV systems are typically exposed to outdoor conditions, making them more susceptible to aging and damage. In this paper, a predictive maintenance approach that integrates digital twin technology with the copula-based model is proposed. This integration enables accurate simulation of the PV system's condition and precise representation of the correlation between the power output of the digital twin and that of the actual system. Given the power output of the digital twin, predictive maintenance is performed based on the conditional cumulative distribution function (CDF) of the actual power output, which is derived from the copula model. A comprehensive case study was conducted to evaluate the performance of the proposed approach named OCAD (Optimal Copula-based Anomaly Detector), which achieved an accuracy of 92.51% and an F1-score of 92.13%. This significantly outperforms conventional models, including SVM, KNN, and ANN, demonstrating the effectiveness of the proposed predictive maintenance strategy. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194588074
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en19112686
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 18
        StartPage: 2686
    Subjects:
      – SubjectFull: Digital twin
        Type: general
      – SubjectFull: Copula functions
        Type: general
      – SubjectFull: Photovoltaic power systems
        Type: general
      – SubjectFull: Statistical correlation
        Type: general
      – SubjectFull: Renewable energy sources
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Outlier detection
        Type: general
      – SubjectFull: Condition-based maintenance
        Type: general
    Titles:
      – TitleFull: Predictive Maintenance in PV Systems: A Copula-Based Approach with Digital Twin Technique.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Zhang, Songjie
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            NameFull: Yang, Xinyi
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            NameFull: Qi, Donglian
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            NameFull: Xu, Zhao
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            NameFull: Wang, Minghao
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            NameFull: Yan, Yunfeng
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
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              Value: 19
            – Type: issue
              Value: 11
          Titles:
            – TitleFull: Energies (19961073)
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