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A Joint Estimation Method of Distribution Network Topology and Line Parameters Based on Power Flow Graph Convolutional Networks.

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Title: A Joint Estimation Method of Distribution Network Topology and Line Parameters Based on Power Flow Graph Convolutional Networks.
Authors: Wang, Yu1 (AUTHOR) wangyu08@stu.scu.edu.cn, Shen, Xiaodong1 (AUTHOR) liujy@scu.edu.cn, Tang, Xisheng2 (AUTHOR) tang@mail.iee.ac.cn, Liu, Junyong1 (AUTHOR)
Source: Energies (19961073). Nov2024, Vol. 17 Issue 21, p5272. 23p.
Subjects: Electrical load, Flowgraphs, Parameter identification, Parameter estimation, Distribution management
Abstract: Accurate identification of network topology and line parameters is essential for effective management of distribution systems. An innovative joint estimation method for distribution network topology and line parameters is presented, utilizing a power flow graph convolutional network (PFGCN). This approach addresses the limitations of traditional methods that rely on costly voltage phase angle measurements. The node correlation principle is applied to construct a node correlation matrix, and a minimum distance iteration algorithm is proposed to generate candidate topologies, which serve as graph inputs for the parameter estimation model. Based on the topological dependencies and convolutional properties of AC power flow equations, a PFGCN model is designed for line parameter estimation. Parameter refinement is achieved through an alternating iterative process of pseudo-trend calculation and neural network training. Training convergence and loss function values are used as feedback to filter and validate candidate topologies, enabling precise joint estimation of both topologies and parameters. The proposed method's accuracy, transferability, and robustness are demonstrated through experiments on the IEEE-33 and modified IEEE-69 distribution systems. Multiple metrics, including MAPE, IAE, MAE, and R2, highlight the proposed method's advantages over Adaptive Ridge Regression (ARR). In the C33 scenario, the proposed method achieves MAPEs of 4.6% for g and 5.7% for b, outperforming the ARR method with MAPEs of 7.1% and 7.9%, respectively. Similarly, in the IC69 scenario, the proposed method records MAPEs of 3.0% for g and 5.9% for b, surpassing the ARR method's 5.1% and 8.3%. [ABSTRACT FROM AUTHOR]
Copyright of Energies (19961073) is the property of MDPI 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.)
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  Data: A Joint Estimation Method of Distribution Network Topology and Line Parameters Based on Power Flow Graph Convolutional Networks.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Yu%22">Wang, Yu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wangyu08@stu.scu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Shen%2C+Xiaodong%22">Shen, Xiaodong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> liujy@scu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Tang%2C+Xisheng%22">Tang, Xisheng</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> tang@mail.iee.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Junyong%22">Liu, Junyong</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Nov2024, Vol. 17 Issue 21, p5272. 23p.
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  Data: <searchLink fieldCode="DE" term="%22Electrical+load%22">Electrical load</searchLink><br /><searchLink fieldCode="DE" term="%22Flowgraphs%22">Flowgraphs</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+identification%22">Parameter identification</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Distribution+management%22">Distribution management</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Accurate identification of network topology and line parameters is essential for effective management of distribution systems. An innovative joint estimation method for distribution network topology and line parameters is presented, utilizing a power flow graph convolutional network (PFGCN). This approach addresses the limitations of traditional methods that rely on costly voltage phase angle measurements. The node correlation principle is applied to construct a node correlation matrix, and a minimum distance iteration algorithm is proposed to generate candidate topologies, which serve as graph inputs for the parameter estimation model. Based on the topological dependencies and convolutional properties of AC power flow equations, a PFGCN model is designed for line parameter estimation. Parameter refinement is achieved through an alternating iterative process of pseudo-trend calculation and neural network training. Training convergence and loss function values are used as feedback to filter and validate candidate topologies, enabling precise joint estimation of both topologies and parameters. The proposed method's accuracy, transferability, and robustness are demonstrated through experiments on the IEEE-33 and modified IEEE-69 distribution systems. Multiple metrics, including MAPE, IAE, MAE, and R2, highlight the proposed method's advantages over Adaptive Ridge Regression (ARR). In the C33 scenario, the proposed method achieves MAPEs of 4.6% for g and 5.7% for b, outperforming the ARR method with MAPEs of 7.1% and 7.9%, respectively. Similarly, in the IC69 scenario, the proposed method records MAPEs of 3.0% for g and 5.9% for b, surpassing the ARR method's 5.1% and 8.3%. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Energies (19961073) is the property of MDPI 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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        Value: 10.3390/en17215272
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        Text: English
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        PageCount: 23
        StartPage: 5272
    Subjects:
      – SubjectFull: Electrical load
        Type: general
      – SubjectFull: Flowgraphs
        Type: general
      – SubjectFull: Parameter identification
        Type: general
      – SubjectFull: Parameter estimation
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      – SubjectFull: Distribution management
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      – TitleFull: A Joint Estimation Method of Distribution Network Topology and Line Parameters Based on Power Flow Graph Convolutional Networks.
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            NameFull: Wang, Yu
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            NameFull: Shen, Xiaodong
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            NameFull: Tang, Xisheng
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            NameFull: Liu, Junyong
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          Dates:
            – D: 01
              M: 11
              Text: Nov2024
              Type: published
              Y: 2024
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            – TitleFull: Energies (19961073)
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