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Research on transmission line planning through massive data mining based on historical survey designs.

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Title: Research on transmission line planning through massive data mining based on historical survey designs.
Authors: Qu, Zhanfei1 (AUTHOR), Dong, Gang2 (AUTHOR), He, Chunhui1 (AUTHOR) hch15532556@163.com, Sun, Qigang1 (AUTHOR), Xie, Dan1 (AUTHOR), Lu, Ling1 (AUTHOR)
Source: Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.). Jul2025, Vol. 25 Issue 4, p3007-3018. 12p.
Subjects: Institute of Electrical & Electronics Engineers, Optimization algorithms, Electric lines, Electric power distribution grids, Power transmission, Data transmission systems, Demand forecasting
Abstract: The growing demand for reliable and efficient power transmission has necessitated innovative solutions for transmission line planning. Despite advancements in energy systems, transmission line planning faces challenges related to optimizing routes and capacities while accommodating historical data variability and demand forecasts. The objective of the study is to utilize massive data mining techniques on historical survey designs to enhance transmission line planning, improving efficiency and reliability in the electrical grid while reducing costs and environmental impact. The study collects historical transmission data, including load forecasts, transmission line specifications, outage records, and demand patterns. The proposed study introduces an Intelligent Grasshopper Optimization Algorithm (IGOA) method for analyzing historical transmission data. This method identifies optimal transmission line placements and capacities, and enhancing decision-making by optimizing multiple objectives, including cost, reliability, and efficiency in power transmission planning. Simulations are conducted on the IEEE 118-bus system, testing various cases to ensure the robustness and efficiency of IGOA approach. The outcomes demonstrate the plans generated through the proposed IGOA strategy yield significantly lower expansion costs compared to traditional transmission line planning models, exhibiting minimal operational infeasibilities that can be easily addressed in short-term expansion planning. This research highlights a robust framework that can be adapted to various energy systems, ultimately supporting more sustainable and reliable power transmission infrastructure. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.) is the property of Sage Publications Inc. 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: Research on transmission line planning through massive data mining based on historical survey designs.
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  Data: <searchLink fieldCode="DE" term="%22Institute+of+Electrical+%26+Electronics+Engineers%22">Institute of Electrical & Electronics Engineers</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+lines%22">Electric lines</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+distribution+grids%22">Electric power distribution grids</searchLink><br /><searchLink fieldCode="DE" term="%22Power+transmission%22">Power transmission</searchLink><br /><searchLink fieldCode="DE" term="%22Data+transmission+systems%22">Data transmission systems</searchLink><br /><searchLink fieldCode="DE" term="%22Demand+forecasting%22">Demand forecasting</searchLink>
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  Label: Abstract
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  Data: The growing demand for reliable and efficient power transmission has necessitated innovative solutions for transmission line planning. Despite advancements in energy systems, transmission line planning faces challenges related to optimizing routes and capacities while accommodating historical data variability and demand forecasts. The objective of the study is to utilize massive data mining techniques on historical survey designs to enhance transmission line planning, improving efficiency and reliability in the electrical grid while reducing costs and environmental impact. The study collects historical transmission data, including load forecasts, transmission line specifications, outage records, and demand patterns. The proposed study introduces an Intelligent Grasshopper Optimization Algorithm (IGOA) method for analyzing historical transmission data. This method identifies optimal transmission line placements and capacities, and enhancing decision-making by optimizing multiple objectives, including cost, reliability, and efficiency in power transmission planning. Simulations are conducted on the IEEE 118-bus system, testing various cases to ensure the robustness and efficiency of IGOA approach. The outcomes demonstrate the plans generated through the proposed IGOA strategy yield significantly lower expansion costs compared to traditional transmission line planning models, exhibiting minimal operational infeasibilities that can be easily addressed in short-term expansion planning. This research highlights a robust framework that can be adapted to various energy systems, ultimately supporting more sustainable and reliable power transmission infrastructure. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.) is the property of Sage Publications Inc. 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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RecordInfo BibRecord:
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        Value: 10.1177/14727978251318062
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 12
        StartPage: 3007
    Subjects:
      – SubjectFull: Institute of Electrical & Electronics Engineers
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
      – SubjectFull: Electric lines
        Type: general
      – SubjectFull: Electric power distribution grids
        Type: general
      – SubjectFull: Power transmission
        Type: general
      – SubjectFull: Data transmission systems
        Type: general
      – SubjectFull: Demand forecasting
        Type: general
    Titles:
      – TitleFull: Research on transmission line planning through massive data mining based on historical survey designs.
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          Name:
            NameFull: Qu, Zhanfei
      – PersonEntity:
          Name:
            NameFull: Dong, Gang
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            NameFull: He, Chunhui
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            NameFull: Sun, Qigang
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            NameFull: Xie, Dan
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            – D: 01
              M: 07
              Text: Jul2025
              Type: published
              Y: 2025
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              Value: 25
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