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A Data-Driven Parameter Inversion Method for Converter Valve Thyristor Levels Based on Time-Frequency-Domain Features.

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Title: A Data-Driven Parameter Inversion Method for Converter Valve Thyristor Levels Based on Time-Frequency-Domain Features.
Authors: Zhu, Yingfeng1 (AUTHOR), Xu, Donglin1,2 (AUTHOR), Li, Ming1,3 (AUTHOR), Li, Chenhao1,2 (AUTHOR), Ren, Jie2 (AUTHOR), Ding, Junqi1,3 (AUTHOR), Xia, Boyang3 (AUTHOR), Pang, Lei3 (AUTHOR) plein911@163.com
Source: Energies (19961073). May2026, Vol. 19 Issue 10, p2357. 19p.
Subject Terms: *Thyristors, *Parameter estimation, *Time-frequency analysis, *Back propagation, *Grey Wolf Optimizer algorithm, *Electronic equipment, *High-voltage direct current transmission, *Wavelet transforms
Abstract: The thyristor level is the basic unit of ultra-high-voltage and extra-high-voltage direct current (DC) converter valves, and its main-circuit parameters are important indicators for characterizing the health status of converter valves. To meet the demand for efficient detection of converter valve thyristor levels, this paper proposes a parameter inversion method for converter valve thyristor levels by combining the time-frequency-domain features of valve voltage and current, temporal characteristics of feedback signals from the thyristor-level monitoring unit, and a Grey Wolf Optimizer–Backpropagation Neural Network (GWO-BPNN). First, a six-pulse converter valve circuit simulation model is established. Based on this model, the original dataset is generated using the Latin hypercube sampling (LHS) method. Wavelet packet decomposition is then used to extract time-frequency-domain features, and dimensionality reduction is carried out by comparing the coefficient of variation and explained variance ratio so as to obtain input data suitable for neural network training. A BP neural network is then trained, and the network parameters are optimized using the Grey Wolf Optimizer to improve the accuracy and convergence speed of parameter inversion. Simulation comparison results show that the GWO-BP method is more efficient than the state equation method and is suitable for efficient inversion of damping parameters in multi-level thyristor systems. After GWO optimization, the maximum inversion errors of both parameters are reduced to below 5%. Compared with BP, GA-BP, and PSO-BP, the proposed GWO-BP model provides the best overall balance between resistance-inversion accuracy and training efficiency. By further incorporating feedback feature signals, the inversion error can be reduced to 1%. The proposed method provides a new technical route for efficient detection of thyristor converter valves and has broad application prospects. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194141472
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
  Group: Ti
  Data: A Data-Driven Parameter Inversion Method for Converter Valve Thyristor Levels Based on Time-Frequency-Domain Features.
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  Data: <searchLink fieldCode="AR" term="%22Zhu%2C+Yingfeng%22">Zhu, Yingfeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Donglin%22">Xu, Donglin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Ming%22">Li, Ming</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Chenhao%22">Li, Chenhao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ren%2C+Jie%22">Ren, Jie</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ding%2C+Junqi%22">Ding, Junqi</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xia%2C+Boyang%22">Xia, Boyang</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pang%2C+Lei%22">Pang, Lei</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> plein911@163.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 10, p2357. 19p.
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  Data: *<searchLink fieldCode="DE" term="%22Thyristors%22">Thyristors</searchLink><br />*<searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br />*<searchLink fieldCode="DE" term="%22Time-frequency+analysis%22">Time-frequency analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Back+propagation%22">Back propagation</searchLink><br />*<searchLink fieldCode="DE" term="%22Grey+Wolf+Optimizer+algorithm%22">Grey Wolf Optimizer algorithm</searchLink><br />*<searchLink fieldCode="DE" term="%22Electronic+equipment%22">Electronic equipment</searchLink><br />*<searchLink fieldCode="DE" term="%22High-voltage+direct+current+transmission%22">High-voltage direct current transmission</searchLink><br />*<searchLink fieldCode="DE" term="%22Wavelet+transforms%22">Wavelet transforms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The thyristor level is the basic unit of ultra-high-voltage and extra-high-voltage direct current (DC) converter valves, and its main-circuit parameters are important indicators for characterizing the health status of converter valves. To meet the demand for efficient detection of converter valve thyristor levels, this paper proposes a parameter inversion method for converter valve thyristor levels by combining the time-frequency-domain features of valve voltage and current, temporal characteristics of feedback signals from the thyristor-level monitoring unit, and a Grey Wolf Optimizer–Backpropagation Neural Network (GWO-BPNN). First, a six-pulse converter valve circuit simulation model is established. Based on this model, the original dataset is generated using the Latin hypercube sampling (LHS) method. Wavelet packet decomposition is then used to extract time-frequency-domain features, and dimensionality reduction is carried out by comparing the coefficient of variation and explained variance ratio so as to obtain input data suitable for neural network training. A BP neural network is then trained, and the network parameters are optimized using the Grey Wolf Optimizer to improve the accuracy and convergence speed of parameter inversion. Simulation comparison results show that the GWO-BP method is more efficient than the state equation method and is suitable for efficient inversion of damping parameters in multi-level thyristor systems. After GWO optimization, the maximum inversion errors of both parameters are reduced to below 5%. Compared with BP, GA-BP, and PSO-BP, the proposed GWO-BP model provides the best overall balance between resistance-inversion accuracy and training efficiency. By further incorporating feedback feature signals, the inversion error can be reduced to 1%. The proposed method provides a new technical route for efficient detection of thyristor converter valves and has broad application prospects. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en19102357
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 19
        StartPage: 2357
    Subjects:
      – SubjectFull: Thyristors
        Type: general
      – SubjectFull: Parameter estimation
        Type: general
      – SubjectFull: Time-frequency analysis
        Type: general
      – SubjectFull: Back propagation
        Type: general
      – SubjectFull: Grey Wolf Optimizer algorithm
        Type: general
      – SubjectFull: Electronic equipment
        Type: general
      – SubjectFull: High-voltage direct current transmission
        Type: general
      – SubjectFull: Wavelet transforms
        Type: general
    Titles:
      – TitleFull: A Data-Driven Parameter Inversion Method for Converter Valve Thyristor Levels Based on Time-Frequency-Domain Features.
        Type: main
  BibRelationships:
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      – PersonEntity:
          Name:
            NameFull: Zhu, Yingfeng
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            NameFull: Xu, Donglin
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            NameFull: Li, Ming
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            NameFull: Li, Chenhao
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            NameFull: Ren, Jie
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            NameFull: Ding, Junqi
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            NameFull: Xia, Boyang
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            NameFull: Pang, Lei
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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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