Turbine-to-turbine prognostics technique for wind farms
원문보기
IPC분류정보
국가/구분
United States(US) Patent
등록
국제특허분류(IPC7판)
G01B-003/00
G01L-005/12
G01L-005/16
G01R-011/30
G06F-019/00
F03D-007/04
F03D-011/00
출원번호
US-0674200
(2012-11-12)
등록번호
US-8924162
(2014-12-30)
발명자
/ 주소
Lapira, Edzel R.
Al-Atat, Hassan
Lee, Jay
출원인 / 주소
University of Cincinnati
대리인 / 주소
Everett, Denise M.
인용정보
피인용 횟수 :
2인용 특허 :
10
초록▼
Methods and systems for predicting an end of life of a wind turbine component including receiving environmental conditions indicative of natural surroundings of wind turbines within a wind turbine farm, receiving component performance metrics indicative of an operation of wind turbines within a wind
Methods and systems for predicting an end of life of a wind turbine component including receiving environmental conditions indicative of natural surroundings of wind turbines within a wind turbine farm, receiving component performance metrics indicative of an operation of wind turbines within a wind turbine farm, and distributing the wind turbines into peer-clusters such that the wind turbines within each of the peer-clusters have similar environmental conditions. The methods and systems further include identifying a low performing wind turbine and a remaining portion of wind turbines within one of the peer-clusters based upon a predicted performance model, processing the component performance metrics of the low performing wind turbine, identifying a critical component of the low performing wind turbine and predicting the end of life of the critical component of the low performing wind turbine.
대표청구항▼
1. A method for predicting an end of life of a wind turbine component wherein a computer processor executes a computer program encoded in a non-transitory computer readable medium containing instructions there for causing the computer processor to perform an operation of transforming electronic data
1. A method for predicting an end of life of a wind turbine component wherein a computer processor executes a computer program encoded in a non-transitory computer readable medium containing instructions there for causing the computer processor to perform an operation of transforming electronic data into a prognostic evaluation, the method comprising: receiving environmental conditions indicative of natural surroundings of wind turbines within a wind turbine farm from environmental sensors;receiving component performance metrics indicative of an operation of wind turbines within a wind turbine farm from performance sensors;distributing the wind turbines into peer-clusters having less than a total number of wind turbines within the wind turbine farm such that the wind turbines within each of the peer-clusters have similar environmental conditions;identifying a highest performing wind turbine, a low performing wind turbine, and a subgroup of higher performing wind turbines within one of the peer-clusters based upon a predicted performance model;the computer processor processing the component performance metrics of the low performing wind turbine and the subgroup of higher performing wind turbines in the peer-cluster to extract fault condition indicators that correlate the component performance metrics to failure modes;identifying a critical component of the low performing wind turbine by contrasting the fault condition indicators of the low performing wind turbine with the subgroup of higher performing wind turbines in the peer-cluster; andpredicting the end of life of the critical component of the low performing wind turbine based upon the component performance metrics of the subgroup of higher performing wind turbine in the peer-cluster. 2. The method of claim 1 further comprising building a health assessment model of the critical component, wherein the health assessment model is built with the component performance metrics of the subgroup of higher performing wind turbines. 3. The method of claim 2, wherein the health assessment model comprises a logistic regression, a statistical pattern recognition, a feature map pattern matching, a neural network, a Gaussian mixture model, a support vector machine, a Bayesian belief network, a hidden Markov model, a self organizing map, or a combination thereof. 4. The method of claim 1 further comprising receiving performance metrics indicative of performance of the wind turbines, wherein the highest performing wind turbine, the low performing wind turbine, and the subgroup of higher performing wind turbines are identified by evaluating the performance metrics of the wind turbines in the peer-clusters against the predicted performance model. 5. The method of claim 1, wherein the performance metrics comprise a comparison of power generation and wind speed. 6. The method of claim 1 further comprising applying a distance measurement technique to determine a probability of defect. 7. The method of claim 1 further comprising scheduling a maintenance procedure comprising taking the wind turbine off line from generating power when a predicted cost of not performing the maintenance procedure is greater than the predicted cost of performing the maintenance procedure. 8. The method of claim 1, wherein the end of life is predicted by an autoregressive moving average, a recurrent neural network, a fuzzy logic, a match matrix, or a combination thereof. 9. The method of claim 1, wherein the environmental conditions comprise wind speed, wind direction, temperature, barometric pressure, humidity, or a combination thereof. 10. The method of claim 1, wherein the wind turbines comprise a bearing, a blade, and a generator. 11. The method of claim 10, wherein the component performance metrics are correlated to the bearing. 12. The method of claim 10, wherein the component performance metrics are correlated to a pitch of the blade. 13. The method of claim 10, wherein the component performance metrics are correlated to a yaw of the blade. 14. The method of claim 10, wherein the component performance metrics are correlated to a power output by the generator. 15. A system for predicting an end of life of a wind turbine component, the system comprising: a computer processor for executing machine readable instructions electronically coupled to a non-transitory computer readable medium encoded with a computer program containing machine readable instructions stored therein for causing the computer processor to perform the machine readable instructions;a wind turbine farm comprising wind turbines for generating energy from wind;environmental sensors located proximate to each of the wind turbines for detecting environmental conditions surrounding the wind turbines; andperformance sensors located proximate to each of the wind turbines for detecting performance metrics correlated with the wind turbines;wherein the computer processor is supplied with data from the environmental sensors and the performance sensors and executes the machine readable instructions of the computer program to: distribute the wind turbines into peer-clusters according to similarities in the environmental conditions, where the peer-clusters have less than a total number of wind turbines within the wind turbine farm;identify a highest performing wind turbine, a low performing wind turbine, and a subgroup of higher performing wind turbines within one of the peer-clusters based upon the performance metrics;process the component performance metrics of the low performing wind turbine and the subgroup of higher performing wind turbines in the peer-cluster to extract fault condition indicators that correlate the component performance metrics to failure modes;identify a critical component of the low performing wind turbine by contrasting the fault condition indicators of the low performing wind turbine with the subgroup of higher performing wind turbines in the peer-cluster; andpredict the end of life of the critical component of the low performing wind turbine based upon the component performance metrics of the subgroup of higher performing wind turbines in the peer-cluster. 16. The system of claim 15, wherein the environmental conditions and/or the performance metrics are processed by a time domain analysis, a frequency domain analysis, a time-frequency analysis, a wavelet/wavelet packet analysis, a principal component analysis, or a combination thereof. 17. The system of claim 15, wherein the component sensors are an accelerometer, a thermocouple, a tachometer, an oil pressure sensor, or an oil temperature sensor. 18. A wind turbine farm that predicts an end of life of a wind turbine component comprising: a plurality of wind turbines, each wind turbine comprising a generator coupled to a blade by a gearbox for generating energy from wind;environmental sensors located proximate to each of the wind turbines for detecting environmental conditions surrounding the wind turbines;performance sensors located proximate to each of the wind turbines for detecting performance metrics correlated with the wind turbines;gearbox sensors located proximate to each of the gearboxes for detecting gearbox performance metrics correlated with the wind turbines; anda computer processor for executing machine readable instructions in a non-transitory computer readable medium for causing the computer processor to perform the machine readable instruction,wherein the processor is supplied with data from the environmental sensors, the performance sensors, and the gearbox sensors and executes the machine readable instructions to: distribute the wind turbines into peer-cluster according to similarities in the environmental conditions, where the peer-clusters have less than a total number of wind turbines within the wind turbine farm;identify a highest performing wind turbine, a low performing wind turbine, and a subgroup of higher performing wind turbines within one of the peer-clusters based upon the performance metrics; andpredict an end of life of the gearbox from the low performing wind turbine according to differences in the gearbox performance metrics between the low performing wind turbine and the subgroup of higher performing wind turbines. 19. The wind turbine farm of claim 18 further comprising processing the gearbox performance metrics to extract fault condition indicators that correlate the gearbox performance metrics to failure modes. 20. The wind turbine farm of claim 19, wherein the failure modes are gear tooth breakage, hub rotating imbalance, bent shaft, bearing race defect, or bent key.
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