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Development of a Flashover Voltage Prediction Model with the Pollution and Conductivity as Factors Using the Response Surface Methodology

Author

Listed:
  • Oussama Ghermoul

    (LGE Laboratory, Electrical Engineering Department, Faculty of Technology, University of M’sila, M’sila 28000, Algeria)

  • Hani Benguesmia

    (LGE Laboratory, Electrical Engineering Department, Faculty of Technology, University of M’sila, M’sila 28000, Algeria)

  • Loutfi Benyettou

    (LGE Laboratory, Electrical Engineering Department, Faculty of Technology, University of M’sila, M’sila 28000, Algeria)

Abstract

In this paper, the response surface methodology (RSM) is used to predict the flashover voltage of a cap and pin 1512L insulator used by SONELGAZ Algerian Power Company (SPE). The pollution and conductivity are studied using a two-level central composite design. MINITAB 19 software is used to perform the regression analysis and analysis of variance (ANOVA) of the data, from which the full quadratic model is developed. The results show that both the pollution and conductivity have a significant effect on the response. The model validation shows the good agreement between the experiment’s obtained results and the predicted results. Therefore, the model could be used to predict the flashover voltage.

Suggested Citation

  • Oussama Ghermoul & Hani Benguesmia & Loutfi Benyettou, 2022. "Development of a Flashover Voltage Prediction Model with the Pollution and Conductivity as Factors Using the Response Surface Methodology," Energies, MDPI, vol. 15(19), pages 1-11, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7161-:d:928597
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    References listed on IDEAS

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    1. K. Venkata Rao & P. B. G. S. N. Murthy, 2018. "Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1533-1543, October.
    2. Shabana Khatoon & Asfar Ali Khan & Mohd Tariq & Basem Alamri & Lucian Mihet-Popa, 2022. "Flashover Voltage Prediction Models under Agricultural and Biological Contaminant Conditions on Insulators," Energies, MDPI, vol. 15(4), pages 1-14, February.
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    Cited by:

    1. Zhijin Zhang & Hang Zhang & Song Yue & Hao Wang, 2023. "Contamination Deposit and Model of Insulator," Energies, MDPI, vol. 16(6), pages 1-3, March.

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