Actuator fault estimation in wind turbine using a modified sliding mode observer based on linear matrix inequality approach

• Autorzy: Taouil Mohammed , El Ougli Abdelghani , Tidhaf Belkassem

Identyfikatory

DOI

10.29354/diag/187887

• Języki publikacji: EN

Abstrakty

EN

This paper presents a fault detection and isolation (FDI) method applied to a wind turbine system. The approach utilizes a nonlinear sliding mode observer (SMO) to effectively reconstruct faults in both the hydraulic pitch actuator and generator torque actuator of the wind turbine. A Linear Matrix Inequality (LMI) optimization approach is employed for the design. The blade pitch angle and generator torque in the wind turbine have significantly different orders of magnitudes, rendering them vulnerable to faults of different magnitudes. This discrepancy poses a challenge for the simultaneous reconstruction of faults. To resolve this challenge, a modification is made to the observer. To examine the effectiveness of the modified SMO, two fault scenarios were considered for the hydraulic pitch actuator and generator torque actuator. In the first case, faults are introduced separately, while in the second case, faults occur simultaneously. Simulation results demonstrate accurate detection, isolation, and reconstruction of these faults, whether in the case of separate or simultaneous fault occurrences.

Słowa kluczowe

EN

actuator fault reconstruction; faults separately; faults simultaneously; modified sliding mode observer

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2024
• Tom: Vol. 25, No. 2
• Strony: art. no 2024211
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Taouil Mohammed

• Electronics and Systems Laboratory - LES, Faculty of Sciences Oujda, Embedded Systems, Renewable Energy and Artificial Intelligence Team, ENSA, Oujda, Morocco

• https://orcid.org/0000-0002-1072-2029

autor

El Ougli Abdelghani

• Computer Science, Signal, Automation and Cognitivism Laboratory (LISAC), Faculty of Science Sidi Mohamed Ben Abdellah University, Fez, Morocco

• https://orcid.org/0000-0001-6737-6094

autor

Tidhaf Belkassem

• Embedded Systems, Renewable Energy and Artificial Intelligence Team, National School of Applied Sciences, Mohammed First University, Oujda, Morocco

• https://orcid.org/0000-0002-9208-295X

Bibliografia

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