naa a22 4500 510822126 CHVBK 20180411083538.0 cr unu---uuuuu 180411e20130601xx s 000 0 eng 10.1007/s12541-013-0141-8 doi (NATIONALLICENCE)springer-10.1007/s12541-013-0141-8 Prognosis-informed wind farm operation and maintenance for concurrent economic and environmental benefits [Elektronische Daten] [Pingfeng Wang, Prasanna Tamilselvan, Janet Twomey, Byeng Youn] 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. Korean Society for Precision Engineering and Springer-Verlag Berlin Heidelberg, 2013 Economic and environmental impact nationallicence Predictive maintenance nationallicence Prognostics nationallicence Wind farm O&M nationallicence O&M : operation and maintenance nationallicence WT : wind turbine nationallicence CBM : condition-based maintenance nationallicence SBI : similarity-based interpolation nationallicence RUL : remaining useful life nationallicence D : current damage level nationallicence d D /dt : rate of damage growth nationallicence d N /dt : load cycles acting per hour on WT nationallicence C : damage coefficient nationallicence Δ K : change in damage-intensity factor nationallicence m : damage exponent nationallicence β : geometry factor nationallicence H s : load factor nationallicence X s : proportionality factor nationallicence C tot : total cost nationallicence C ins : total inspection cost nationallicence C rep : total repair cost nationallicence C fail : total failure cost nationallicence C trans : total transportation cost nationallicence C CMS : condition monitoring system installation cost nationallicence C prod : total cost incurred due to production loss nationallicence R : rate of interest nationallicence b : cost per KWH nationallicence n days : number of days required to perform repair nationallicence n KWH : number of units of power produced nationallicence CC : carbon credits nationallicence d : total number of downtime hours nationallicence p : loss of power production per hour in KWH nationallicence e : CO2 emissions in tons per KWH generated by coal power plant nationallicence W j : inverse of the sum of squared error nationallicence D j b( t i) : jth damage-growth path data nationallicence PoD : probability of detection of damage nationallicence P 0 : maximum probability of detection nationallicence λ : expected value of smallest detectable damage nationallicence Wang Pingfeng Department of Industrial and Manufacturing Engineering, Wichita State University, 67260, Wichita, Kansas, USA aut Tamilselvan Prasanna Department of Industrial and Manufacturing Engineering, Wichita State University, 67260, Wichita, Kansas, USA aut Twomey Janet Department of Industrial and Manufacturing Engineering, Wichita State University, 67260, Wichita, Kansas, USA aut Youn Byeng School of Mechanical & Aerospace Engineering, Seoul National University, 151-742, Seoul, South Korea aut International Journal of Precision Engineering and Manufacturing Korean Society for Precision Engineering 14/6(2013-06-01), 1049-1056 2234-7593 14:6<1049 2013 14 12541 https://doi.org/10.1007/s12541-013-0141-8 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s12541-013-0141-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Wang Pingfeng Department of Industrial and Manufacturing Engineering, Wichita State University, 67260, Wichita, Kansas, USA aut NATIONALLICENCE 700 1- Tamilselvan Prasanna Department of Industrial and Manufacturing Engineering, Wichita State University, 67260, Wichita, Kansas, USA aut NATIONALLICENCE 700 1- Twomey Janet Department of Industrial and Manufacturing Engineering, Wichita State University, 67260, Wichita, Kansas, USA aut NATIONALLICENCE 700 1- Youn Byeng School of Mechanical & Aerospace Engineering, Seoul National University, 151-742, Seoul, South Korea aut NATIONALLICENCE 773 0- International Journal of Precision Engineering and Manufacturing Korean Society for Precision Engineering 14/6(2013-06-01), 1049-1056 2234-7593 14:6<1049 2013 14 12541 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer