alexa Model-based Fault Diagnosis For Wind Turbine System
ISSN: 2169-0316

Industrial Engineering & Management
Open Access

OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

Share This Page

Additional Info

Loading Please wait..

International Summit on Industrial Engineering
December 08-10, 2014 DoubleTree by Hilton Hotel San Francisco Airport, USA

Sarah Odofin
Accepted Abstracts: Ind Eng Manage
DOI: 10.4172/2169-0316.S1.006
The concern to develop wind turbines as a low carbon source of renewable energy is paramount to the fast expansion of renewable energy for power production. Proper analytical condition monitoring technique to observe the state of system could minimize damage to the whole turbine. Faults are inevitable in automated system of which failures could be severe depending on the kind of machine and the circumstances of the fault interruption. The main aim is to increase reliability, cost effective for modern dynamic systems could improve industrial automatic system availability as well as achieving a better system performance. The main challenge in model-based fault detection and diagnosis FDD is to diagnose incipient and abrupt faults in complex dynamic systems considering a practical system input and output measurements. Several techniques have been proposed to improve the performance of fault diagnosis is a captivating research challenge to advance of the robustness to fault detection. Model-based FDI has been studied for over 20 years; this can be used to either boost safety or accomplish a more reasonable current level of safety. A genetic algorithm (GA) is an artificial intelligence to increase the FDD performance to optimize an observer. A better analysis have been identified to demonstrate the propose approach.
Sarah Odofin is a 2nd year research student at Northumbria University Newcastle. Currently studying fault diagnosis of wind turbine, a member of IEEE and presently a group member of control, power and energy research group and Optical wireless communications group. She has attended various conferences, seminars workshop and made some paper presentation.
image PDF   |   image HTML

Relevant Topics

Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version