Quantification of economic and environmental benefits for predictive wind farm operation and maintenance -- Restricted access to full text

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Issue Date
2012-04-18
Embargo End Date
Authors
Tamilselvan, Prasanna
Advisor
Wang, Pingfeng
Citation

Prasanna Tamilselvan. (2012). Quantification of Economic and Environmental Benefits for Predictive Wind Farm Operation and Maintenance. -- In Proceedings: 8th Annual Symposium: Graduate Research and Scholarly Projects. Wichita, KS: Wichita State University, p.47-48

Abstract

Maintaining wind turbines in top operating condition ensures not only a continuous revenue generation but a reduction in electric power drawn from non-renewable and more polluting sources. Tremendous advances in high performance sensing and advanced signal processing technology enable the development of failure prognosis tools for wind turbines to detect, diagnose, and predict the system-wide effects of failure events. However, the advantages of utilizing failure prognosis have not been fully recognized by the current wind industry, mainly due to the difficulty of quantitatively measuring the benefits of failure prognosis for the wind farm operation and maintenance (O&M) decision making. This paper presents a generic probabilistic framework for the quantification of economic and environmental benefits of failure prognosis for the O&M decision making of wind farms. In the presented framework, probabilistic damage growth models will be used to characterize individual wind turbine performance degradation process, whereas the economic losses measured by monetary values and environmental impacts measured by unified carbon credits will be accumulated over the wind farm O&M processes. The efficacy of proposed methodology will be demonstrated with case studies comparing the prognosis informed maintenance strategy and the traditional condition based maintenance policy.

Table of Content
Description
Paper presented to the 8th Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Marcus Welcome Center, Wichita State University, April 18, 2012.
Research completed at the Department of Industrial and Manufacturing Engineering, College of Engineering
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