Quantification of economic and environmental benefits for prognosis informed wind farm operation and maintenance

Prasanna Tamilselvan, Pingfeng Wang, Janet Twomey

Research output: Contribution to conferencePaperpeer-review


Advances in high performance sensing and 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. Although prognostics can provide valuable information for proactive actions in preventing system failures, the benefits have not been fully utilized for the operation and maintenance decision making of wind turbines. This paper presents a generic failure prognosis informed decision making tool for wind farm operation and maintenance while considering the predictive failure information of individual turbine and its uncertainty. In the presented approach, the probabilistic damage growth model is used to characterize individual wind turbine performance degradation and failure prognostics, whereas the economic loss measured by monetary values and environmental performance measured by unified carbon credits are considered in the decision making process. Based on the customized wind farm information inputs, the developed decision making methodology can be used to identify optimum and robust strategies for wind farm operation and maintenance in order to maximize the economic and environmental benefits concurrently. The efficacy of proposed prognosis informed maintenance strategy is compared with the condition based maintenance strategy and demonstrated with the case study.

Original languageEnglish (US)
Number of pages10
StatePublished - 2012
Externally publishedYes
Event62nd IIE Annual Conference and Expo 2012 - Orlando, FL, United States
Duration: May 19 2012May 23 2012


Other62nd IIE Annual Conference and Expo 2012
Country/TerritoryUnited States
CityOrlando, FL


  • Economic and environmental impact
  • Predictive maintenance
  • Prognostics
  • Wind farm O & M

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering


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