Intelligent fault diagnosis of power transmission line using fuzzy logic and artificial neural network

• Autorzy: Touati Khaled Omer Mokhtar , Boudiaf Mohamed , Merzouk Imad , Hafaifa Ahmed

Identyfikatory

DOI

10.29354/diag/156495

• Języki publikacji: EN

Abstrakty

EN

In the industrial sector, transmission lines are an important part of the electrical grid. Thus it is important to protect it from all the different faults that may occur as soon as possible to supply the electric power continuously. This paper presents a modern solutions and a comparative study of fault detection and identification in electrical transmission lines using artificial neural network (ANN) compare to the fuzzy logic. Faults in transmission line of various types have been created using simulation model. An intelligent monitoring system (IFD: Intelligent Fault Diagnosis) was used at both ends of a 230 kV overhead transmission line, voltage and current measurements exploited as indicator data for this system. Both approaches were found to be robust, accurate and reliable to detect the fault when it occurs, to determine the fault type short circuit or opening of a power line (open circuit), to locate the fault and to determine which phase was faulted.

Słowa kluczowe

EN

power system diagnosis; fault detection; electrical transmission lines; ANN; fuzzy logic

PL

system elektroenergetyczny; diagnozowanie; linia elektroenergetyczna; wykrywanie uszkodzeń; logika rozmyta; sztuczne sieci neuronowe

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2022
• Tom: Vol. 23, No. 4
• Strony: art. no. 2022410
• Opis fizyczny: Bibliogr. 35 poz., rys., tab.

Twórcy

autor

Touati Khaled Omer Mokhtar

• Applied Automation and Industrial Diagnostics Laboratory, Faculty of Sciences and Technology, Ziane Achour University, Djelfa, Algeria

autor

Boudiaf Mohamed

• Applied Automation and Industrial Diagnostics Laboratory, Faculty of Sciences and Technology, Ziane Achour University, Djelfa, Algeria

• https://orcid.org/0000-0002-1845-8656

autor

Merzouk Imad

• Applied Automation and Industrial Diagnostics Laboratory, Faculty of Sciences and Technology, Ziane Achour University, Djelfa, Algeria

• https://orcid.org/0000-0003-1539-1414

autor

Hafaifa Ahmed

• Applied Automation and Industrial Diagnostics Laboratory, Faculty of Sciences and Technology, Ziane Achour University, Djelfa, 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).