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

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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.

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Abbreviations

O&M:

operation and maintenance

WT:

wind turbine

CBM:

condition-based maintenance

SBI:

similarity-based interpolation

RUL:

remaining useful life

D :

current damage level

dD/dt:

rate of damage growth

dN/dt:

load cycles acting per hour on WT

C :

damage coefficient

ΔK :

change in damage-intensity factor

m :

damage exponent

β:

geometry factor

H s :

load factor

X s :

proportionality factor

C tot :

total cost

C ins :

total inspection cost

C rep :

total repair cost

C fail :

total failure cost

C trans :

total transportation cost

C CMS :

condition monitoring system installation cost

C prod :

total cost incurred due to production loss

R :

rate of interest

b :

cost per KWH

n days :

number of days required to perform repair

n KWH :

number of units of power produced

CC :

carbon credits

d :

total number of downtime hours

p :

loss of power production per hour in KWH

e :

CO2 emissions in tons per KWH generated by coal power plant

W j :

inverse of the sum of squared error

D j b(t i):

jth damage-growth path data

PoD :

probability of detection of damage

P 0 :

maximum probability of detection

λ :

expected value of smallest detectable damage

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Authors and Affiliations

  1. Department of Industrial and Manufacturing Engineering, Wichita State University, Wichita, Kansas, 67260, USA

    Pingfeng Wang, Prasanna Tamilselvan & Janet Twomey

  2. School of Mechanical & Aerospace Engineering, Seoul National University, Seoul, South Korea, 151-742

    Byeng Dong Youn

Authors
  1. Pingfeng Wang
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  2. Prasanna Tamilselvan
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  4. Byeng Dong Youn
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Correspondence to Pingfeng Wang.

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Wang, P., Tamilselvan, P., Twomey, J. et al. Prognosis-informed wind farm operation and maintenance for concurrent economic and environmental benefits. Int. J. Precis. Eng. Manuf. 14, 1049–1056 (2013). https://doi.org/10.1007/s12541-013-0141-8

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  • DOI: https://doi.org/10.1007/s12541-013-0141-8

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