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Photovoltaic Expansion Perception Method Based on GWO-PSO-Optimized Robust Extreme Learning Machine.

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Title: Photovoltaic Expansion Perception Method Based on GWO-PSO-Optimized Robust Extreme Learning Machine.
Authors: He, Houyu1 (AUTHOR) 20234460306@stu.usc.edu.cn, Sheng, Yifa1 (AUTHOR)
Source: Energies (19961073). May2026, Vol. 19 Issue 10, p2350. 21p.
Subject Terms: *Grey Wolf Optimizer algorithm, *Particle swarm optimization, *Photovoltaic power systems, *Extreme learning machines
Abstract: Addressing the safety risks to the distribution network caused by the unauthorized capacity expansion behaviors of distributed photovoltaic (PV) users, this paper proposes a PV capacity expansion detection model based on the gray wolf–particle swarm optimization hybrid optimization robust extreme learning machine (GWO-PSO-MELM). Firstly, the PV power generation data is preprocessed using cosine similarity and dynamic time warping (DTW) to reduce the impact of regional meteorological differences. Secondly, by combining the global search capability of the Gray Wolf Algorithm (GWO) with the fast convergence characteristics of the particle swarm optimization (PSO) algorithm, the hidden layer weights and biases of the robust extreme learning machine (MELM) are optimized to enhance the model's robustness to outliers. Finally, the dynamic diagnosis of capacity expansion intensity and time nodes is achieved by calculating the illegal capacity expansion coefficient K. Experiments based on actual PV data from Changsha show that the probability density analysis of the illegal capacity expansion coefficient can identify capacity expansion behaviors as low as 10%, with a positioning error of capacity expansion time nodes of ≤4%. In actual cases, three illegal capacity expansion users were successfully detected, and the detection deviation remained small under different capacity expansion ratios, verifying the effectiveness of the proposed method in PV capacity expansion detection. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194141465
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Photovoltaic Expansion Perception Method Based on GWO-PSO-Optimized Robust Extreme Learning Machine.
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22He%2C+Houyu%22">He, Houyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 20234460306@stu.usc.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Sheng%2C+Yifa%22">Sheng, Yifa</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 10, p2350. 21p.
– Name: Subject
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  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Grey+Wolf+Optimizer+algorithm%22">Grey Wolf Optimizer algorithm</searchLink><br />*<searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Photovoltaic+power+systems%22">Photovoltaic power systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Extreme+learning+machines%22">Extreme learning machines</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Addressing the safety risks to the distribution network caused by the unauthorized capacity expansion behaviors of distributed photovoltaic (PV) users, this paper proposes a PV capacity expansion detection model based on the gray wolf–particle swarm optimization hybrid optimization robust extreme learning machine (GWO-PSO-MELM). Firstly, the PV power generation data is preprocessed using cosine similarity and dynamic time warping (DTW) to reduce the impact of regional meteorological differences. Secondly, by combining the global search capability of the Gray Wolf Algorithm (GWO) with the fast convergence characteristics of the particle swarm optimization (PSO) algorithm, the hidden layer weights and biases of the robust extreme learning machine (MELM) are optimized to enhance the model's robustness to outliers. Finally, the dynamic diagnosis of capacity expansion intensity and time nodes is achieved by calculating the illegal capacity expansion coefficient K. Experiments based on actual PV data from Changsha show that the probability density analysis of the illegal capacity expansion coefficient can identify capacity expansion behaviors as low as 10%, with a positioning error of capacity expansion time nodes of ≤4%. In actual cases, three illegal capacity expansion users were successfully detected, and the detection deviation remained small under different capacity expansion ratios, verifying the effectiveness of the proposed method in PV capacity expansion detection. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en19102350
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 21
        StartPage: 2350
    Subjects:
      – SubjectFull: Grey Wolf Optimizer algorithm
        Type: general
      – SubjectFull: Particle swarm optimization
        Type: general
      – SubjectFull: Photovoltaic power systems
        Type: general
      – SubjectFull: Extreme learning machines
        Type: general
    Titles:
      – TitleFull: Photovoltaic Expansion Perception Method Based on GWO-PSO-Optimized Robust Extreme Learning Machine.
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          Name:
            NameFull: He, Houyu
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            NameFull: Sheng, Yifa
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          Dates:
            – D: 15
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961073
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              Value: 19
            – Type: issue
              Value: 10
          Titles:
            – TitleFull: Energies (19961073)
              Type: main
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