A Comparison of Edge Detectors in the Framework of Wake Pattern Modeling for Wind Turbines



Published Nov 3, 2020
Yanjun Yan James Z. Zhang Hayrettin Bora Karayaka


To monitor wind turbine health, wind farm operators can take advantage of the historical SCADA (supervisory control and data acquisition) data to generate the wake pattern beforehand
for each wind turbine, and then decide in real time whether observed reduction in power generation is due to wake or true faults. In our earlier efforts, we proposed an effective wake
pattern modeling approach based on edge detector using Linear Prediction (LP) with entropy-thresholding, and smoothing using Empirical Mode Decomposition (EMD) on the wind
speed difference plots. In this paper, we compare the LP based edge detector with two other predominant edge detectors, Sobel and Canny edge detectors, to quantitatively justify
the appropriateness and effectiveness of the LP based edge detector in wind turbine wake pattern analysis. We generate a fused wake model for the turbine of interest with multiple neighboring turbines, and then analyze the wake effect on turbine power generation. With a fused wake pattern, we do not need to identify the individual source of wake any more. We
expect that wakes cause reduced wind speed and hence reduced power generation, but we have also observed from the SCADA data that the wind turbines in wake zones tend to overreact when the wind speed is not yet close to the highwind- shut-down threshold, which causes further power generation loss.

Abstract 154 | PDF Downloads 171



signal processing, Wind farms, data processing, algorithms, wake analysis, edge detector

Ainslie, J. F. (1988). Calculating the flowfield in the wake of wind turbines. Journal of Wind Engineering and Industrial Aerodynamics, 27(1-3), 213-224.
Barthelmie, R. J., Larsen, G. C., Frandsen, S. T., Folkerts, L., Rados, K., Pryor, S. C., . . . Schepers, G. (2006). Comparison of wake model simulations with offshore wind turbine wake profiles measured by sodar. Journal of Atmospheric and Oceanic Technology, 23(7), 888-901.
Beaucage, P., Brower, M., Robinson, N., & Alonge, C. (2012). Overview of six commercial and research wake models for large offshore wind farms. In EWEA 2012.
Belyaev, A. (2011). On implicit image derivatives and their applications. In Proceedings of the british machine vision conference (pp. 72.1–72.12). BMVA Press.
Burton, T., Sharpe, D., Jenkins, N., & Bossanyi, E. (Eds.). (2001). Wind energy handbook. Wiley.
Canny, J. (1986). A computational approach to edge detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, PAMI-8(6), 679-698.
Clive, P. J. M., Dinwoodie, I., &Quail, F. (2012). Direct measurement of wind turbine wakes using remote sensing. SgurrEnergy Ltd Technical Report.
Farid, H., & Simoncelli, E. (2004). Differentiation of discrete multidimensional signals. Image Processing, IEEE Transactions on, 13(4), 496-508.
Flandrin, P., Rilling, G., & Goncalves, P. (2004). Empirical mode decomposition as a filter bank. Signal Processing Letters, IEEE, 11(2), 112-114.
Hassan, U. (1992). A wind tunnel investigation of the wake structure within small wind turbine farms. Department of Energy in ETSU, UK.
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., . . . Liu, H. H. (1998). The empirical mode decomposition and the hilbert spectrum for nonlinear and nonstationary time series analysis. Proc. Roy. Soc. Lond. A, 903-1005.
Lang, S., & McKeogh, E. (2011). Lidar and sodar measurements of wind speed and direction in upland terrain for wind energy purposes. Remote Sensing, 3, 1871-1901.
Makhoul, J. (1975). Linear prediction: A tutorial review. Proceedings of the IEEE, 63(4), 561-580.
Nilsson, K. (2012). Numerical computations of wind turbine wakes and wake interaction: Optimization and control. Trita-MEK, KTH, Mechanics(2012:18), vi, 56.
Quarton, D., & Ainslie, J. (1990). Turbulence in wind turbine wakes. Wind Engineering, 14(1).
ur Rehman, N., & Mandic, D. P. (2011). Filter bank property of multivariate empirical mode decomposition. Signal Processing, IEEE Transactions on, 59(5), 2421 - 2426.
US Dept of Commerce, National Oceanic and Atmospheric Administration, & National Weather Service. (2002). http://www.ndbc.noaa.gov/educate/seabreeze.shtml. NDBC Science Education Pages.
Wu, Y.-T., & Port-Agel, F. (2011). Large-eddy simulation of wind-turbine wakes: Evaluation of turbine parametrisations. Boundary-Layer Meteorology, 138(3), 345-366.
Yan, Y., Kamath, G., Osadciw, L. A., Benson, G., Legac, P., Johnson, P., & White, E. (2009). Fusion for modeling wake effects on wind turbines. In Proceedings of 12th international conference on information fusion. Seattle, Washington, USA.
Yan, Y., & Zhang, J. (2014). Using edge-detector to model wake effects on wind turbines. In IEEE international conference on prognostics and health management 2014.
Zhang, J. Z., & Punch, A. J. (2012). Pattern extraction of sonar images using an lpc edge detector with entropy thresholding. OCEANS, 2012, 1-8.
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