Picture of rolled up £5 note

Open Access research that shapes economic thinking...

Strathprints makes available scholarly Open Access content by the Fraser of Allander Institute (FAI), a leading independent economic research unit focused on the Scottish economy and based within the Department of Economics. The FAI focuses on research exploring economics and its role within sustainable growth policy, fiscal analysis, energy and climate change, labour market trends, inclusive growth and wellbeing.

The open content by FAI made available by Strathprints also includes an archive of over 40 years of papers and commentaries published in the Fraser of Allander Economic Commentary, formerly known as the Quarterly Economic Commentary. Founded in 1975, "the Commentary" is the leading publication on the Scottish economy and offers authoritative and independent analysis of the key issues of the day.

Explore Open Access research by FAI or the Department of Economics - or read papers from the Commentary archive [1975-2006] and [2007-2018]. Or explore all of Strathclyde's Open Access research...

Nonlinear observer-based fault detection and isolation for wind turbines

Katebi, Reza and Hwas, Abdulhamed Moh Suliman (2014) Nonlinear observer-based fault detection and isolation for wind turbines. In: IEEE 22nd Mediterranean Conference on Control and Automation. IEEE, Piscataway, New Jersey. (In Press)

[img]
Preview
PDF (HwasKatebi-MED2014-fault-detection-preprint)
HwasKatebi_MED2014_fault_detection_preprint.pdf
Preprint

Download (199kB) | Preview

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.