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Online Prediction Method for Power System Frequency Response Analysis Based on Swarm Intelligence Fusion Model

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Title: Online Prediction Method for Power System Frequency Response Analysis Based on Swarm Intelligence Fusion Model
Authors: Lin Xu, Li Li, Meiying Wang, Xiangxu Wang, Yicong Li, Weidong Li, Kuanjiu Zhou
Source: IEEE Access, Vol 11, Pp 13519-13532 (2023)
Publisher Information: IEEE, 2023.
Publication Year: 2023
Collection: LCC:Electrical engineering. Electronics. Nuclear engineering
Subject Terms: Transient response prediction, integrated model, system frequency response, LSTM, improved sparrow search algorithm, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
More Details: Instability at transient frequency caused by faults in complex power systems is one of the greatest threats to operational safety. By analyzing the frequency response of power system in real-time and adopting control strategies promptly, power system accidents can be efficiently prevented. While existing online analysis methods integrate physical-driven and data-driven methodologies, they do not effectively utilize frequency timing characteristics. Consequently, a swarm intelligence fusion model, which integrates physical-driven and data-driven methods, is proposed as an improved frequency response analysis method. The transient frequency affecting components are separated into primary state variables and system time series data based on the properties of the time sequence. To preserve the actual relationship of the electrical mechanism model, the system frequency response (SFR) model is used as the physical-driven method for the primary state variables of the system. The Long Short Term Memory (LSTM) network was used as the data-driven method to extract timing features and correct the SFR model’s prediction using the system time series data as input. The two methods are combined using the bootstrap mode to form the fusion model, and the structure of the model is optimized using an improved sparrow search algorithm (ISSA), a swarm intelligence optimization algorithm. The model structure is adapted autonomously, implementing a method for online frequency response analysis. The simulation on the New England 39-bus system has verified that the method can quickly and accurately calculate the dynamic process of frequency response after a large-scale disturbance.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2169-3536
Relation: https://ieeexplore.ieee.org/document/10036418/; https://doaj.org/toc/2169-3536
DOI: 10.1109/ACCESS.2023.3242557
Access URL: https://doaj.org/article/12184f706f2c44059c8d6e6cae6d9a79
Accession Number: edsdoj.12184f706f2c44059c8d6e6cae6d9a79
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  Data: Online Prediction Method for Power System Frequency Response Analysis Based on Swarm Intelligence Fusion Model
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  Data: <searchLink fieldCode="AR" term="%22Lin+Xu%22">Lin Xu</searchLink><br /><searchLink fieldCode="AR" term="%22Li+Li%22">Li Li</searchLink><br /><searchLink fieldCode="AR" term="%22Meiying+Wang%22">Meiying Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Xiangxu+Wang%22">Xiangxu Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Yicong+Li%22">Yicong Li</searchLink><br /><searchLink fieldCode="AR" term="%22Weidong+Li%22">Weidong Li</searchLink><br /><searchLink fieldCode="AR" term="%22Kuanjiu+Zhou%22">Kuanjiu Zhou</searchLink>
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  Data: IEEE Access, Vol 11, Pp 13519-13532 (2023)
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  Data: 2023
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  Data: LCC:Electrical engineering. Electronics. Nuclear engineering
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– Name: Abstract
  Label: Description
  Group: Ab
  Data: Instability at transient frequency caused by faults in complex power systems is one of the greatest threats to operational safety. By analyzing the frequency response of power system in real-time and adopting control strategies promptly, power system accidents can be efficiently prevented. While existing online analysis methods integrate physical-driven and data-driven methodologies, they do not effectively utilize frequency timing characteristics. Consequently, a swarm intelligence fusion model, which integrates physical-driven and data-driven methods, is proposed as an improved frequency response analysis method. The transient frequency affecting components are separated into primary state variables and system time series data based on the properties of the time sequence. To preserve the actual relationship of the electrical mechanism model, the system frequency response (SFR) model is used as the physical-driven method for the primary state variables of the system. The Long Short Term Memory (LSTM) network was used as the data-driven method to extract timing features and correct the SFR model’s prediction using the system time series data as input. The two methods are combined using the bootstrap mode to form the fusion model, and the structure of the model is optimized using an improved sparrow search algorithm (ISSA), a swarm intelligence optimization algorithm. The model structure is adapted autonomously, implementing a method for online frequency response analysis. The simulation on the New England 39-bus system has verified that the method can quickly and accurately calculate the dynamic process of frequency response after a large-scale disturbance.
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        Value: 10.1109/ACCESS.2023.3242557
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      – Text: English
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      – SubjectFull: Transient response prediction
        Type: general
      – SubjectFull: integrated model
        Type: general
      – SubjectFull: system frequency response
        Type: general
      – SubjectFull: LSTM
        Type: general
      – SubjectFull: improved sparrow search algorithm
        Type: general
      – SubjectFull: Electrical engineering. Electronics. Nuclear engineering
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      – SubjectFull: TK1-9971
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      – TitleFull: Online Prediction Method for Power System Frequency Response Analysis Based on Swarm Intelligence Fusion Model
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            NameFull: Lin Xu
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