Fault diagnosis-based observers using Kalman filters and Luenberger estimators: Application to the pitch system fault actuators

• Autorzy: Zemali Zakaria , Cherroun Lakhmissi , Hadroug Nadji , Nadour Mohamed , Hafaifa Ahmed

Identyfikatory

DOI

10.29354/diag/161307

• Języki publikacji: EN

Abstrakty

EN

This paper aims to present a robust fault diagnosis structure-based observers for actuator faults in the pitch part system of the wind turbine benchmark. In this work, two linear estimators have been proposed and investigated: the Kalman filter and the Luenberger estimator for observing the output states of the pitch system in order to generate the appropriate residual between the measured positions of blades and the estimated values. An inference step as a decision block is employed to decide the existence of faults in the process, and to classify the detected faults using a predetermined threshold defined by upper and lower limits. All actuator faults in the pitch system of the horizontal wind turbine benchmark are studied and investigated. The obtained simulation results show the ability of the proposed diagnosis system to determine effectively the occurred faults in the pitch system. Estimation of the output variables is effectively realized in both situations: without and with the occurrence of faults in the studied process. A comparison between the two used observers is demonstrated.

Słowa kluczowe

EN

fault detection; estimation; pitch system; Kalman filter; Luenberger observer

PL

wykrywanie uszkodzeń; estymacja; filtr Kalmana; obserwator Luenbergera

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2023
• Tom: Vol. 24, No. 1
• Strony: art. no. 2022110
• Opis fizyczny: Bibliogr. 38 poz., rys., tab.

Twórcy

autor

Zemali Zakaria

• Applied Automation and Industrial Diagnostics Laboratory, Faculty of Sciences and Technology, University of Djelfa, 17000 DZ, Algeria

autor

Cherroun Lakhmissi

• Applied Automation and Industrial Diagnostics Laboratory, Faculty of Sciences and Technology, University of Djelfa, 17000 DZ, Algeria

• https://orcid.org/0000-0002-4709-0352

autor

Hadroug Nadji

• Applied Automation and Industrial Diagnostics Laboratory, Faculty of Sciences and Technology, University of Djelfa, 17000 DZ, Algeria

autor

Nadour Mohamed

• Applied Automation and Industrial Diagnostics Laboratory, Faculty of Sciences and Technology, University of Djelfa, 17000 DZ, Algeria

autor

Hafaifa Ahmed

• Applied Automation and Industrial Diagnostics Laboratory, Faculty of Sciences and Technology, University of Djelfa, 17000 DZ, Algeria
• Department of Electrical and Electronics Engineering, Nisantasi University, 34398 Sarıyer, İstanbul, Turkey

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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).