Nonlinear observer-based fault detection and isolation for wind turbines

Reza Katebi, Abdulhamed Moh Suliman Hwas

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

This paper is concerned with the development of a novel nonlinear observer-based scheme for early Fault Detection and Isolation (FDI) in wind turbines. The method is based on designing a nonlinear observer using State Dependent Differential Riccati Equation (SDDRE) and a nonlinear model of the 5MW wind turbine. The fault detection system is designed and optimized to be most sensitive to system faults and least sensitive to system disturbances and noises. The comparison of system outputs with nonlinear observer outputs are given to demonstrate good estimation performance. The residual generator based on the nonlinear observer is also employed to develop a monitoring system. Simulation results presented to illustrate that the proposed method is robust and can detect and isolate a fault or multi-faults in sensors of the wind turbine.
LanguageEnglish
Title of host publicationIEEE 22nd Mediterranean Conference on Control and Automation
Place of PublicationPiscataway, New Jersey
PublisherIEEE
Publication statusAccepted/In press - Jun 2014

Fingerprint

Fault detection
Wind turbines
Riccati equations
Monitoring
Sensors

Keywords

  • fault diagnosis
  • nonlinear systems
  • renewable energy and sustainability
  • state dependent differential riccati equation

Cite this

Katebi, R., & Hwas, A. M. S. (Accepted/In press). Nonlinear observer-based fault detection and isolation for wind turbines. In IEEE 22nd Mediterranean Conference on Control and Automation Piscataway, New Jersey: IEEE.
Katebi, Reza ; Hwas, Abdulhamed Moh Suliman. / Nonlinear observer-based fault detection and isolation for wind turbines. IEEE 22nd Mediterranean Conference on Control and Automation. Piscataway, New Jersey : IEEE, 2014.
@inproceedings{5c530ac98d8b4ffcb1c0c62e2a3a8499,
title = "Nonlinear observer-based fault detection and isolation for wind turbines",
abstract = "This paper is concerned with the development of a novel nonlinear observer-based scheme for early Fault Detection and Isolation (FDI) in wind turbines. The method is based on designing a nonlinear observer using State Dependent Differential Riccati Equation (SDDRE) and a nonlinear model of the 5MW wind turbine. The fault detection system is designed and optimized to be most sensitive to system faults and least sensitive to system disturbances and noises. The comparison of system outputs with nonlinear observer outputs are given to demonstrate good estimation performance. The residual generator based on the nonlinear observer is also employed to develop a monitoring system. Simulation results presented to illustrate that the proposed method is robust and can detect and isolate a fault or multi-faults in sensors of the wind turbine.",
keywords = "fault diagnosis, nonlinear systems, renewable energy and sustainability, state dependent differential riccati equation",
author = "Reza Katebi and Hwas, {Abdulhamed Moh Suliman}",
year = "2014",
month = "6",
language = "English",
booktitle = "IEEE 22nd Mediterranean Conference on Control and Automation",
publisher = "IEEE",

}

Katebi, R & Hwas, AMS 2014, Nonlinear observer-based fault detection and isolation for wind turbines. in IEEE 22nd Mediterranean Conference on Control and Automation. IEEE, Piscataway, New Jersey.

Nonlinear observer-based fault detection and isolation for wind turbines. / Katebi, Reza; Hwas, Abdulhamed Moh Suliman.

IEEE 22nd Mediterranean Conference on Control and Automation. Piscataway, New Jersey : IEEE, 2014.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

TY - GEN

T1 - Nonlinear observer-based fault detection and isolation for wind turbines

AU - Katebi, Reza

AU - Hwas, Abdulhamed Moh Suliman

PY - 2014/6

Y1 - 2014/6

N2 - This paper is concerned with the development of a novel nonlinear observer-based scheme for early Fault Detection and Isolation (FDI) in wind turbines. The method is based on designing a nonlinear observer using State Dependent Differential Riccati Equation (SDDRE) and a nonlinear model of the 5MW wind turbine. The fault detection system is designed and optimized to be most sensitive to system faults and least sensitive to system disturbances and noises. The comparison of system outputs with nonlinear observer outputs are given to demonstrate good estimation performance. The residual generator based on the nonlinear observer is also employed to develop a monitoring system. Simulation results presented to illustrate that the proposed method is robust and can detect and isolate a fault or multi-faults in sensors of the wind turbine.

AB - This paper is concerned with the development of a novel nonlinear observer-based scheme for early Fault Detection and Isolation (FDI) in wind turbines. The method is based on designing a nonlinear observer using State Dependent Differential Riccati Equation (SDDRE) and a nonlinear model of the 5MW wind turbine. The fault detection system is designed and optimized to be most sensitive to system faults and least sensitive to system disturbances and noises. The comparison of system outputs with nonlinear observer outputs are given to demonstrate good estimation performance. The residual generator based on the nonlinear observer is also employed to develop a monitoring system. Simulation results presented to illustrate that the proposed method is robust and can detect and isolate a fault or multi-faults in sensors of the wind turbine.

KW - fault diagnosis

KW - nonlinear systems

KW - renewable energy and sustainability

KW - state dependent differential riccati equation

UR - http://www.unipa.it/med14/

M3 - Conference contribution book

BT - IEEE 22nd Mediterranean Conference on Control and Automation

PB - IEEE

CY - Piscataway, New Jersey

ER -

Katebi R, Hwas AMS. Nonlinear observer-based fault detection and isolation for wind turbines. In IEEE 22nd Mediterranean Conference on Control and Automation. Piscataway, New Jersey: IEEE. 2014