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Adaptive Neuro-Fuzzy Inference System Application of Flashover Voltage of High-Voltage Polluted Insulator

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Abstract

The paper proposes an adaptive neuro-fuzzy inference system called ANFIS for predicting the flashover voltage of external insulators. High voltage insulators were the subject of actual testing, which produced a database for the application of artificial intelligence ideas. The experiments were conducted using different concentrations of synthetic pollution (distilled brine), with each concentration denoting the amount of contamination per milliliter of area. The database offered flashover voltage values for various pollution levels and electrical conductivity levels in each isolation zone. Adaptive neuro-fuzzy inference employed a hybrid learning algorithm to determine suitable membership functions, minimizing the root mean square error as the performance criterion. The primary parameters affecting flashover voltage were identified: applied high voltage, conductivity of the artificial impurity, and amount of impurity in the insulation. For both training and test data, precise predictions were obtained by using membership functions in the shape of a triangle with three fuzzy sets. During testing, the technique demonstrated a low mean absolute percentage error (0.027011) and a high coefficient of determination (0.999997049). Comparison with practical tests yielded a root mean square error of 0.0128623, confirming the effectiveness of the Adaptive Neuro-Fuzzy Inference System in estimating the critical flashover voltage for newly designed insulators under different operating conditions.

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References

  1. Benguesmia H, M’ziou N, Boubakeur A (2018) Simulation of the potential and electric field distribution on high voltage insulator using the finite element method. Diagnostyka 19(2):41–52. https://doi.org/10.29354/diag/86414

    Article  Google Scholar 

  2. Savaghebi M, Gholami A, Jalilian A, Hooshyar H (2008) A neuro-fuzzy approach for estimation of time-to-flashover characteristic of polluted insulators. In: 2008 IEEE 2nd international power and energy conference, pp. 1485–1487

  3. Gençoğlu MT, Cebeci M (2008) The pollution flashover on high voltage insulators. Electr Power Syst Res 78:1914–1921. https://doi.org/10.1016/j.epsr.2008.03.019

    Article  Google Scholar 

  4. Benguesmia H (2018) Modeling an insulator under pollution conditions under alternative voltage 50 Hz. Ph.D. Thesis, Biskra University, Biskra, Algeria, 2018. http://thesis.univbiskra.dz/3907/1/These_Hani_Benguesmia_2018%282%29-compress%C3%A9.pdf

  5. Al-geelania NA, Piaha MAM, Shaddad RQ (2012) Characterization of acoustic signals due to surface discharges on HV glass insulators using wavelet radial basis function neural networks. Appl Soft Comput 12:1239. https://doi.org/10.1016/j.asoc.2011.12.018

    Article  Google Scholar 

  6. Asimakopoulou GE, Kontargyri VT, Tsekouras GJ, Asimakopoulou FE, Gonos IF, Stathopulos IA (2009) Artificial neural network optimisation methodology for the estimation of the critical flashover voltage on insulators. IET Sci Meas Technol 3:90. https://doi.org/10.1049/iet-smt:20080009

    Article  Google Scholar 

  7. Jang JSR (1993) ANFIS adaptive network based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685. https://doi.org/10.1109/21.256541

    Article  Google Scholar 

  8. Xue-bo J, Jiang-feng W, Hui-yan Z, Li-hong C (2013) ANFIS model for time series prediction. Appl Mech Mater 386:1411–1414. https://doi.org/10.4028/www.scientific.net/AMM.385-386.1411

    Article  Google Scholar 

  9. Armaghani DJ, Asteris PG (2021) A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength. Neural Comput Appl 33:4501–4532. https://doi.org/10.1007/s00521-020-05244-4

    Article  Google Scholar 

  10. Tiwari S, Babbar R, Kaur G (2018) Performance evaluation of two ANFIS models for predicting water quality index of river Satluj (India). Adv Civ Eng 2018:8971079. https://doi.org/10.1155/2018/8971079

    Article  Google Scholar 

  11. Savaghebi M, Gholami A, Jalilian A, Hooshyar H (2008) In: IEEE 2nd international power and energy conference (PECon) (Johor Baharu, Malaysia, 2008), p. 1485

  12. Bahramifar A, Shirkhani R, Mohammadi M (2013) An ANFIS based approach for predicting the manning roughness coefficient in alluvial channels at the bank-full stage. Int J Eng 26(2):177–186. https://doi.org/10.5829/idosi.ije.2013.26.02b.08

    Article  Google Scholar 

  13. Jang JSR, Sun CT (1997) Neuro- fuzzy and soft computing a computational approach to learning and machine intelligence. IEEE Trans Autom Control 42:1482–1484. https://doi.org/10.1109/TAC.1997.633847

    Article  Google Scholar 

  14. Ejiofor CI, Okengwu UA (2017) Adaptive neuro fuzzy inference system (ANFIS): MATLAB simulation of breast cancer experimental data. IOSR J Comput Eng 19(4):53–60. https://doi.org/10.9790/0661-1904055360

    Article  Google Scholar 

  15. Chopra S, Dhiman G, Sharma A, Shabaz M, Shukla P, Arora M (2021) Taxonomy of adaptive neuro-fuzzy inference system in modern engineering sciences. Comput Intell Neurosci. https://doi.org/10.1155/2021/6455592

    Article  PubMed  PubMed Central  Google Scholar 

  16. Bensaber BA, Diaz GP, Lahrouni Y (2020) Design and modeling an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of a security index in VANET. J Comput Sci 47:101234. https://doi.org/10.1109/ISCC47284.2019.8969687

    Article  MathSciNet  Google Scholar 

  17. Haznedar B, Kalinli A (2018) Training ANFIS structure using simulated annealing algorithm for dynamic systems identification. Neurocomputing 302:66–74. https://doi.org/10.1016/j.neucom.2018.04.006

    Article  Google Scholar 

  18. Bourek Y, M’Ziou N, Benguesmia H (2018) Prediction of flashover voltage of high-voltage polluted insulator using artificial intelligence. Trans Electr Electron Mater 19:59–68. https://doi.org/10.1007/s42341-018-0010-3

    Article  Google Scholar 

  19. Ibrahim A (2004) Fuzzy logic for embedded systems applications. Newnes

  20. Maddala GS (2001) Introduction to econometrics, 3rd edn. John Wiley & Sons, Hoboken

    Google Scholar 

  21. Çaydaş U, Hasçalık A, Ekici S (2009) An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM. Expert Syst Appl 36:6135–6139. https://doi.org/10.1016/j.eswa.2008.07.019

    Article  Google Scholar 

  22. Ibrahim K, Hamdan WK (2015) Application of adaptive neuro-fuzzy inference system for prediction of surface roughness in incremental sheet metal forming process. Eng Tech J. https://doi.org/10.30684/etj.2015.101907

    Article  Google Scholar 

  23. Al-Haydari ISJ (2018) A neuro-fuzzy and neural network approach for rutting potential prediction of asphalt mixture based on creep test. Al-Nahrain J Eng Sci 21(2):275–284. https://doi.org/10.29194/NJES21020275

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Applied Sciences, University of Ouargla, Road Ghardaia, 30000, Ouargla, Algeria

    Amel Belkebir & Yacine Bourek

  2. LGE Laboratory, Faculty of Technology, University of M’sila, M’sila, Algeria

    Hani Benguesmia

  3. Electrical Engineering Department, Faculty of Technology, University of M’sila, M’sila, Algeria

    Hani Benguesmia

Authors
  1. Amel Belkebir
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  2. Yacine Bourek
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  3. Hani Benguesmia
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Correspondence to Amel Belkebir.

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Belkebir, A., Bourek, Y. & Benguesmia, H. Adaptive Neuro-Fuzzy Inference System Application of Flashover Voltage of High-Voltage Polluted Insulator. J. Electr. Eng. Technol. (2024). https://doi.org/10.1007/s42835-024-01862-3

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