Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

MCP_ES_2012_Jie

302 views

Published on

This paper develops a hybrid Measure-Correlate-Predict (MCP) strategy to predict the long term wind resource variations at a farm site. The hybrid MCP method uses the recorded data of multiple reference stations to estimate the long term wind condition at the target farm site. The weight of each reference station in the hybrid strategy is determined based on: (i) the distance and (ii) the elevation difference between the target farm site and each reference station. The applicability of the proposed hybrid strategy is investigated using four different MCP methods: (i) linear regression; (ii) variance ratio; (iii) Weibull scale; and (iv) Artificial Neural Networks (ANNs). To implement this method, we use the hourly averaged wind data recorded at six stations in North Dakota between the year 2008 and 2010. The station Pillsbury is selected as the target farm site. The recorded data at the other five stations
(Dazey, Galesbury, Hillsboro, Mayville and Prosper) is used as reference station data. Three sets of performance metrics are used to evaluate the hybrid MCP method. The first set of metrics analyze the statistical performance, including the mean wind speed, the wind speed variance, the root mean squared error, and the maximum absolute error. The second set of metrics evaluate the distribution of long term wind speed; to this end, the Weibull distribution and the Multivariate and Multimodal Wind Distribution (MMWD) models are adopted in this paper. The third set of metrics analyze the energy production capacity and the efficiency of the wind farm. The results illustrate that the many-to-one correlation in such a hybrid approach can provide more reliable prediction of the long term onsite wind variations, compared to one-to-one correlations.

Published in: Education
  • Be the first to comment

  • Be the first to like this

MCP_ES_2012_Jie

  1. 1. A Hybrid Measure-Correlate-Predict Method for Wind Resource Assessment Jie Zhang*, Souma Chowdhury*, Achille Messac# and Luciano Castillo** * Rensselaer Polytechnic Institute, Department of Mechanical, Aerospace, and Nuclear Engineering # Syracuse University, Department of Mechanical and Aerospace Engineering ** Texas Tech University, Department of Mechanical Engineering ASME 2012 6th International Conference on Energy Sustainability July 23-26, 2012 San Diego, CA
  2. 2. Wind Resource Assessment  Wind resource assessment is the assessment of the potential of developing a feasible wind energy project at a given site.  In general, wind resource assessment includes:  Onsite wind conditions measurement  Correlations between onsite meteorological towers to fill in missing data  Correlations between long term weather stations and short term onsite meteorological towers  Analysis of the wind shear and its variations  Modeling of the distribution of wind conditions  Prediction of the available energy at the site 2
  3. 3. Measure-Correlate-Predict (MCP) • Measure-Correlate-Predict (MCP) method: predicting the long term wind resource at target sites using the short term (1 or 2 year) onsite data, and the co-occurring data at nearby meteorological stations. • The accuracy of long term predictions using MCP methods is subject to:  The availability of a nearby meteorological station, and its distance from the site  The length of the correlation time-period  The uncertainty associated with a specific correlation methodology 3
  4. 4. Measure-Correlate-Predict (MCP) 4
  5. 5. Research Motivation  Existing Measure-Correlate-Predict Methods include: • Linear Regression Method1,2; • Variance Ratio Method2,3; • Weibull Scale Method3; • Mortimer Method5; • Artificial Neural Networks (ANNs)4,5; and • Support Vector Regression6,7.  The existing MCP methods predict the long term wind data at the farm site using wind data at one reference station.  Current MCP methods do not consider the distance and the elevation difference between the target site and the reference stations.  How to use recorded wind data from multiple nearby reference stations to better predict the wind conditions at the target site? 5 1: Velázquez et al. 2: Perea et.al 3: Carta and Velázquez 4: Mohandes et al. 5: Sheppard 6: Mohandes et al. 7: Zhao et al.
  6. 6. Research Objective Develop and explore the applicability of a hybrid Measure-Correlate- Predict method that adaptively combines wind information from multiple weather stations.  The contribution of each reference station in the hybrid strategy is based on: (i) the distance and (ii) the elevation difference between the target farm site and the reference weather stations. 6
  7. 7. Presentation Outline 7 • Development of the Hybrid MCP Method • Performance Evaluation Metrics • Case Study: Stations in North Dakota • Concluding Remarks and Future Work
  8. 8. Hybrid MCP Method A weighted summation of the MCP predictions from individual weather stations: 8 푦 = 푛 푖=1 푤푖푓푖 (푥) Where: n: is the number of reference sites; 푓푖 (푥): represents the 푖푡ℎ MCP model which estimates the farm site wind condition using the 푖푡ℎ reference site data; and 푤푖: represents the weight of the 푖푡ℎ MCP model. 푤푖 = 푔 (Δ푑푖 , Δℎ푖 ) Where: Δ푑푖: is the distance between the farm site and the 푖푡ℎ reference site; Δℎ푖: is the elevation difference between the farm site and the 푖푡ℎ reference site.
  9. 9. Hybrid MCP Method 9 Weights selection method: 푤푖 = 1 2(푛 − 1) × 푛 Δℎ푖 푗=1,푗≠푖 푛 Δℎ푖 푗=1 + 푛 Δ푑푖 푗=1,푗≠푖 푛 Δ푑푖 푗=1 Weights of each reference station:  Decreases with increasing distance from the target site  Decreases with increasing altitude difference from the target site
  10. 10. Individual Measure-Correlate-Predict Methods  Five MCP Methods are investigated: • Linear Regression Method • Variance Ratio Method • Weibull Scale Method x: reference site; y: target farm site. 10
  11. 11. Individual Measure-Correlate-Predict Methods • Artificial Neural Networks (ANNs) • Support Vector Regression: 11
  12. 12. Accuracy Metrics: Statistical Measures  The ratio of mean wind speeds:  The ratio of wind speed variances:  Root Mean Squared Error (RMSE):  Maximum Absolute Error (MAE): 12
  13. 13. Accuracy Metrics: Wind Farm Output  We compare the annual averaged power generation estimated from the actual long-term wind data and the wind data predicted by the MCP methods.  9-turbine wind farm  3x3 (7D/3D) array layout  We use the power generation model from the Unrestricted Wind Farm Layout Optimization (UWFLO) methodology*. 13 Features of the GE-1.5MW-XLE and GE-2.5MW-XL turbines *Chowdhury et al., Renewable Energy 2012, and ES-FuellCell 2011
  14. 14. Case Study: Selection of Stations 14 Station Latitude (deg) Longitude (deg) Elevation (m) Dazey 47.183 -98.138 439 Galesburg 47.21 -97.431 331 Hillsboro 47.353 -96.922 270 Mayville 47.498 -97.262 290 Pillsbury 47.225 -97.791 392 Prosper 47.002 -97.115 284
  15. 15. Accuracy of Predicted Wind Data 15 Maximum Absolute Error (MAE) The length of correlation period (hours) The length of correlation period (hours) RMSE MAE Root Mean Squared Error (RMSE)  The average RMSE value of hybrid MCP methods is approximately 35% smaller than that of traditional MCP methods.  The average MAE value of hybrid MCP methods is approximately 21% smaller than that of traditional MCP methods.
  16. 16. Accuracy of the Overall Distribution of the Predicted Wind Data 16 The ratio of wind speed variances The length of correlation period (hours) The length of correlation period (hours) Variance Ratio Mean Ratio The ratio of mean wind speeds  The closer the value of the ratio is to one, the more accurate is the estimated wind pattern  The four hybrid MCP methods perform best when the correlation period is between 6000-8500 hours (approx. 8-12 months).
  17. 17. Results and Discussion: Wind Farm Metrics 17 The length of correlation period (hours) Power generation: GE-2.5MW-XL Farm Layout Power generation (W)  The hybrid curves (solid lines) are closer to the actual power generation curve (black line) than the individual MCP prediction curve (dashed lines).  In most cases, the power generation is overestimated when using the wind data predicted by MCP methods.
  18. 18. Concluding Remarks 18  This paper developed a hybrid MCP strategy to predict the long term wind resource information at a farm, using the recorded data of multiple reference stations.  The contribution of each weather station depends on the distance and elevation difference of the site from the reference station.  Two primary sets of performance metrics are used to evaluate the hybrid MCP method: (i) statistical metrics, and (iii) wind farm performance metrics.  The results showed that:  Using wind data from multiple reference stations has the potential to better predict the long term wind condition at the targeted farm site; and  The power generation is generally overestimated using the data predicted by MCP methods.
  19. 19. Future Work  A more comprehensive hybrid strategy should include wind direction information.  The uncertainty in the MCP method should also be characterized and analyzed. 19
  20. 20. Acknowledgement • I would like to acknowledge my research adviser Prof. Achille Messac, and my co-adviser Prof. Luciano Castillo for their immense help and support in this research. • I would also like to thank my friend and colleague Souma Chowdhury for leading this research. • I would also like to thank NSF for supporting this research. 20
  21. 21. Questions and Comments 21 Thank you
  22. 22. Existing Measure-Correlate-Predict Methods  Existing Measure-Correlate-Predict Methods include: • Linear Regression Method; • Variance Ratio Method; • Weibull Scale Method; • Mortimer Method; and • Artificial Neural Networks (ANNs) • Vector Regression Method (two-dimensional linear regression)  Limitations of existing MCP methods include: • Do not consider topography; • Do not include distance between monitoring stations; and • Only use one reference station to predict the target farm site wind condition. 22
  23. 23. Performance Metrics  Wind Distribution Metrics  Weibull distribution  Multivariate and Multimodal Wind Distribution (MMWD) 23 • Kernel Density Estimation • Multivariate Kernel Density Estimation • Optimal Bandwidth Matrix Selection
  24. 24. Results and Discussion: Wind Distribution Metrics 24 Normalized Weibull k parameter Normalized Weibull c parameter  The solid red&blue&pink lines (hybrid linear regression method, hybrid variance ratio, and hybrid SVR) agrees more with the record data distribution (black line) than the dashed red&blue&pink lines (linear regression method, variance ratio, and SVR). Wind distribution
  25. 25. Results and Discussion: Mixing Combinations • Each station can be combined into the hybrid MCP method with: • linear regression; • variance ratio; • neural network; or • SVR method • 1024 (which is equal to 45) different combinations are investigated. 25 Mean Ratio Variance Ratio
  26. 26. Results and Discussion: Mixing Combinations RMSE MAE Power generation: GE-1.5MW-XLE Power generation: GE-2.5MW2-6XL  The average value of RMSE over the length of correlation period varies 4.91% over the 1024 hybrid MCP models.  The average value of the power generation with GE-1.5MW-XLE turbines over the length of correlation period varies 5.57% over the 1024 hybrid MCP models.

×