Prognosis-informed wind farm operation and maintenance for concurrent economic and environmental benefits

Pingfeng Wang, Prasanna Tamilselvan, Janet Twomey, Byeng Dong Youn

Research output: Contribution to journalArticlepeer-review

Abstract

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 an 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 customized wind farm information input, the developed decision-making methodology can be used to identify optimum and robust strategies for wind farm operation and maintenance in order to maximize economic and environmental benefits concurrently. The efficacy of the proposed prognosis-informed maintenance strategy is compared with the condition-based maintenance strategy and demonstrated with a wind farm case study.

Original languageEnglish (US)
Pages (from-to)1049-1056
Number of pages8
JournalInternational Journal of Precision Engineering and Manufacturing
Volume14
Issue number6
DOIs
StatePublished - Jun 2013
Externally publishedYes

Keywords

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

ASJC Scopus subject areas

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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