| Peer-Reviewed

Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor

Received: 21 February 2022     Accepted: 14 March 2022     Published: 13 May 2022
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Abstract

One of the applications that machine learning can offer to the world of Engineering and Fluid Mechanics in particular is the calibration of models making it possible to approximate the representation of a particular phenomenon. Indeed, the computational cost generated by some fluid mechanics models pushes scientists to use other models close to the original models but less computationally intensive in order to facilitate their handling. Among the different approaches used: machine learning coupled with some optimization methods and algorithms in order to reduce the computation cost induced. This paper proposes a new framework called OPTI-ENS: a new flexible, optimized and improved method, to calibrate a physical model, called the wake oscillator (WO), which simulates the vibratory behaviors of overhead line conductors. An approximation of a heavy and complex model called the strip theory (ST) model. OPTI-ENS is composed of an ensemble machine learning algorithm (ENS) and an optimization algorithm of the WO model so that the WO model can generate the adequate training data as input to the ENS model. ENS model will therefore take as input the data from the WO model and output the data from the ST model. As a benchmark, a series of Machine learning models have been implemented and tested. The OPTI-ENS algorithm was retained with a best Coefficient of determination (R2 Score) of almost 0.7 and a Root mean square error (RMSE) of 7.57e-09. In addition, this model is approximately 170 times faster (in terms of calculation time) than an ENS model without optimization of the generation of training data by the WO model. This type of approach therefore makes it possible to calibrate the WO model so that simulations of the behavior of overhead line conductors are carried out only with the WO model.

Published in International Journal of Information and Communication Sciences (Volume 7, Issue 2)
DOI 10.11648/j.ijics.20220702.11
Page(s) 18-42
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Machine Learning, Numerical Simulation, Fluid Mechanics, Model Calibration, Overhead Line Conductor

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    Hamdi Amroun, Fikri Hafid, Mehdi Ammi. (2022). Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor. International Journal of Information and Communication Sciences, 7(2), 18-42. https://doi.org/10.11648/j.ijics.20220702.11

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    Hamdi Amroun; Fikri Hafid; Mehdi Ammi. Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor. Int. J. Inf. Commun. Sci. 2022, 7(2), 18-42. doi: 10.11648/j.ijics.20220702.11

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    Hamdi Amroun, Fikri Hafid, Mehdi Ammi. Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor. Int J Inf Commun Sci. 2022;7(2):18-42. doi: 10.11648/j.ijics.20220702.11

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  • @article{10.11648/j.ijics.20220702.11,
      author = {Hamdi Amroun and Fikri Hafid and Mehdi Ammi},
      title = {Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor},
      journal = {International Journal of Information and Communication Sciences},
      volume = {7},
      number = {2},
      pages = {18-42},
      doi = {10.11648/j.ijics.20220702.11},
      url = {https://doi.org/10.11648/j.ijics.20220702.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijics.20220702.11},
      abstract = {One of the applications that machine learning can offer to the world of Engineering and Fluid Mechanics in particular is the calibration of models making it possible to approximate the representation of a particular phenomenon. Indeed, the computational cost generated by some fluid mechanics models pushes scientists to use other models close to the original models but less computationally intensive in order to facilitate their handling. Among the different approaches used: machine learning coupled with some optimization methods and algorithms in order to reduce the computation cost induced. This paper proposes a new framework called OPTI-ENS: a new flexible, optimized and improved method, to calibrate a physical model, called the wake oscillator (WO), which simulates the vibratory behaviors of overhead line conductors. An approximation of a heavy and complex model called the strip theory (ST) model. OPTI-ENS is composed of an ensemble machine learning algorithm (ENS) and an optimization algorithm of the WO model so that the WO model can generate the adequate training data as input to the ENS model. ENS model will therefore take as input the data from the WO model and output the data from the ST model. As a benchmark, a series of Machine learning models have been implemented and tested. The OPTI-ENS algorithm was retained with a best Coefficient of determination (R2 Score) of almost 0.7 and a Root mean square error (RMSE) of 7.57e-09. In addition, this model is approximately 170 times faster (in terms of calculation time) than an ENS model without optimization of the generation of training data by the WO model. This type of approach therefore makes it possible to calibrate the WO model so that simulations of the behavior of overhead line conductors are carried out only with the WO model.},
     year = {2022}
    }
    

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  • TY  - JOUR
    T1  - Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor
    AU  - Hamdi Amroun
    AU  - Fikri Hafid
    AU  - Mehdi Ammi
    Y1  - 2022/05/13
    PY  - 2022
    N1  - https://doi.org/10.11648/j.ijics.20220702.11
    DO  - 10.11648/j.ijics.20220702.11
    T2  - International Journal of Information and Communication Sciences
    JF  - International Journal of Information and Communication Sciences
    JO  - International Journal of Information and Communication Sciences
    SP  - 18
    EP  - 42
    PB  - Science Publishing Group
    SN  - 2575-1719
    UR  - https://doi.org/10.11648/j.ijics.20220702.11
    AB  - One of the applications that machine learning can offer to the world of Engineering and Fluid Mechanics in particular is the calibration of models making it possible to approximate the representation of a particular phenomenon. Indeed, the computational cost generated by some fluid mechanics models pushes scientists to use other models close to the original models but less computationally intensive in order to facilitate their handling. Among the different approaches used: machine learning coupled with some optimization methods and algorithms in order to reduce the computation cost induced. This paper proposes a new framework called OPTI-ENS: a new flexible, optimized and improved method, to calibrate a physical model, called the wake oscillator (WO), which simulates the vibratory behaviors of overhead line conductors. An approximation of a heavy and complex model called the strip theory (ST) model. OPTI-ENS is composed of an ensemble machine learning algorithm (ENS) and an optimization algorithm of the WO model so that the WO model can generate the adequate training data as input to the ENS model. ENS model will therefore take as input the data from the WO model and output the data from the ST model. As a benchmark, a series of Machine learning models have been implemented and tested. The OPTI-ENS algorithm was retained with a best Coefficient of determination (R2 Score) of almost 0.7 and a Root mean square error (RMSE) of 7.57e-09. In addition, this model is approximately 170 times faster (in terms of calculation time) than an ENS model without optimization of the generation of training data by the WO model. This type of approach therefore makes it possible to calibrate the WO model so that simulations of the behavior of overhead line conductors are carried out only with the WO model.
    VL  - 7
    IS  - 2
    ER  - 

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Author Information
  • Department of Computer Sciences & Artificial Intelligence, University of Paris 8, Saint-Denis, France

  • Department of Computer Sciences & Artificial Intelligence, University of Paris 8, Saint-Denis, France

  • Department of Computer Sciences & Artificial Intelligence, University of Paris 8, Saint-Denis, France

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