Particle swarm optimization of a neural network model for predicting the flashover voltage on polluted cap and pin insulator

• Autorzy: Belkebir Amel , Bourek Yacine , Benguesmia Hani

Identyfikatory

DOI

10.29354/diag/154051

• Języki publikacji: EN

Abstrakty

EN

This paper proposes training an artificial neural network (ANN) by a particle swarm optimization (PSO) technique to predict the flashover voltage of outdoor insulators. The analysis follows a series of real-world tests on high-voltage insulators to form a database for implementing artificial intelligence concepts. These tests are performed in various degrees of artificial contamination (distilled brine). Each contamination level shows the amount of contamination in milliliters per area of the isolator. The acquisition database provides values of flashover voltage corresponding to their electrical conductivity in each isolation zone and different degrees of artificial contamination. The results show that ANN trained by PSO can not only provide better prediction results, but also reduce the amount of computation efforts. It is also a more powerful model because: it does not get stuck in a local optimum. In addition, it also has the advantages of simple logic, simple implementation, and underlying intelligence. Compared to the results obtained by practical tests, the results obtained present that the PSO-ANN technique is very effective to predict flashover of high-voltage polluted insulators.

Słowa kluczowe

EN

flashover voltage; particle swarm optimization; prediction; artificial pollution; neural network

PL

napięcie przeskoku; optymalizacja roju cząstek; prognozowanie; sieć neuronowa

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2022
• Tom: Vol. 23, No. 3
• Strony: art. no 2022309
• Opis fizyczny: Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Belkebir Amel

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

autor

Bourek Yacine

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

autor

Benguesmia Hani

• Laboratoire de Génie Electrique (LGE), Université de M’sila, M’sila, Algérie
• Electrical Engineering Department, Faculty of Technology, University of M’sila, M’sila, Algeria

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).