Intelligent Wind Turbine Power Curve Modelling Using the Third Version of Cultural Algorithm (CA3)

arman goudarzi, Andrew G Swanson, Mehdi Kazemi, Keyou Wang

Abstract


third version of cultural algorithm (CA3), error analysis, mathematical modelling, quadratic Gaussian function, wind turbine generator power curve (WTGPC).The wind turbine generator power curve (WTGPC) gives the relationship between the wind speed and power output of the wind turbine at any given time. The power curves, which are usually provided by the manufacturer company, are mainly used in forecasting, energy planning and performance monitoring of wind turbines. The WTGPC model plays a significant role in the control and monitoring of wind farms as well as playing a role in the wind farms power injection to the grid. This paper presents a comprehensive analysis of several methods of modelling the WTGPC, with respect to four commercial wind turbines; 330, 900, 2000 and 3050 kW. In the first step, the proposed method of the study, based on quadratic Gaussian function, is compared to several developed mathematical models by using error measurement techniques including mean square error (MSE) and residual analysis. The accuracy of the proposed method has then been improved by means of the third version of cultural algorithm (CA3) through the optimization of the proposed method coefficients. The ultimate performance of the compared methods has been investigated by the normalized root mean squared error (NRMSE), where the proposed method of the study shows an excellent performance for modelling of wind turbine power curves.

Total Views: 193

Keywords


third version of cultural algorithm (CA3); error analysis; mathematical modelling; quadratic Gaussian function; wind turbine generator power curve (WTGPC).

Full Text:

6087

References


K. Mohammadi, O. Alavi, A. Mostafaeipour et al., “Assessing different parameters estimation methods of Weibull distribution to compute wind power density,” Energy Conversion and Management, vol. 108, pp. 322-335, Jan 15, 2016.

M. Kazemi, P. Siano, D. Sarno et al., “Evaluating the impact of sub-hourly unit commitment method on spinning reserve in presence of intermittent generators,” Energy, vol. 113, pp. 338-354, Oct 15, 2016.

M. Lydia, A. I. Selvakumar, S. S. Kumar et al., “Advanced Algorithms for Wind Turbine Power Curve Modeling,” Ieee Transactions on Sustainable Energy, vol. 4, no. 3, pp. 827-835, Jul, 2013.

J. M. Hu, and J. Z. Wang, “Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression,” Energy, vol. 93, pp. 1456-1466, Dec 15, 2015.

W. Hernandez, A. Méndez, J. L. Maldonado-Correa et al., “Modeling of a Robust Confidence Band for the Power Curve of a Wind Turbine,” Sensors, vol. 16, no. 12, 2016.

H. Kurss, and W. K. Kahn, “A Note on Reflector Arrays,” Ieee Transactions on Antennas and Propagation, vol. Ap15, no. 5, pp. 692-&, 1967.

S. P. Binguac, “On the compatibility of adaptive controllers ” in in Proc. 4th Annu. Allerton Conf. Circuits and Systems Theory, New York, 1994, pp. 8-16.

T. Ouyang, A. Kusiak, and Y. He, “Modeling wind-turbine power curve: A data partitioning and mining approach,” Renewable Energy, vol. 105, pp. 1-8, 2017.

D. Villanueva, and A. E. Feijoo, “Reformulation of parameters of the logistic function applied to power curves of wind turbines,” Electric Power Systems Research, vol. 137, pp. 51-58, Aug, 2016.

S. Shokrzadeh, M. J. Jozani, and E. Bibeau, “Wind Turbine Power Curve Modeling Using Advanced Parametric and Nonparametric Methods,” Ieee Transactions on Sustainable Energy, vol. 5, no. 4, pp. 1262-1269, Oct, 2014.

A. Kusiak, H. Y. Zheng, and Z. Song, “On-line monitoring of power curves,” Renewable Energy, vol. 34, no. 6, pp. 1487-1493, Jun, 2009.

A. K. Das, and B. M. Mazumdar, "A comparative study on normalized parametric models for wind turbine power curve," Research-gate, 2015.

M. Xu, P. Pinson, Z. X. Lu et al., “Adaptive robust polynomial regression for power curve modeling with application to wind power forecasting,” Wind Energy, vol. 19, no. 12, pp. 2321-2336, Dec, 2016.

S. Y. Yip, C. P. Tan, W. T. Chong et al., "Power optimization model of adjustable guide-vane for an exhaust wind energy recovery system." pp. 1847- 1852.

R. Zarate-Minano, M. Anghel, and F. Milano, “Continuous wind speed models based on stochastic differential equations,” Applied Energy, vol. 104, pp. 42-49, Apr, 2013.

A. Goudarzi, I. E. Davidson, A. Ahmadi et al., "Intelligent Analysis of Wind Turbine Power Curve." pp. 1-7.

C. Carrillo, A. F. O. Montano, J. Cidras et al., “Review of power curve modelling for wind turbines,” Renewable & Sustainable Energy Reviews, vol. 21, pp. 572-581, May, 2013.

V. Thapar, G. Agnihotri, and V. K. Sethi, “Critical analysis of methods for mathematical modelling of wind turbines,” Renewable Energy, vol. 36, no. 11, pp. 3166-3177, Nov, 2011.

P. B. Daniels, “The Fitting, Acceptance, and Processing of Standard Curve Data in Automated Immunoassay Systems, as Exemplified by the Serono Sr1 Analyzer,” Clinical Chemistry, vol. 40, no. 4, pp. 513-517, Apr, 1994.

V. Sohoni, S. C. Gupta, and R. K. Nema, “A comparative analysis of wind speed probability distributions for wind power assessment of four sites,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 24, pp. 4724 – 4735, 2016.

V. Sohoni, S. C. Gupta, and R. K. Nema, “A critical review on wind turbine power curve modelling techniques and their applications in wind based energy systems,” Journal of Energy, vol. 2016, pp. 1-19, 2016.

M. Ragheb, and A. M. Ragheb, "Wind turbine theory – the Betz equation and optimal rotor tip speed ratio," Fundamental and Advanced Topics in Wind Power: INTECH, 2011.

A. Goudrazi, A. G. Swanson, J. Van Coller et al., “Smart real-time scheduling of generating units in an electricity market considering environmental aspects and physical constraints of generators,” Applied Energy, vol. 189, pp. 667-696, 2017.

H. F. Zhang, J. Z. Zhou, N. Fang et al., “Daily hydrothermal scheduling with economic emission using simulated annealing technique based multi-objective cultural differential evolution approach,” Energy, vol. 50, pp. 24-37, Feb 1, 2013.

B. Bhattacharya, D. Mandal, and N. Chakraborty, “A multi-objective optimization based on cultural algorithm for economic dispatch with environmental constraints,” International Journal of Scientific and Engineering Research, vol. 3, pp. 1-8, 2012.

A. Goudarzi, A. Ahmadi, A. G. Swanson et al., “Non-Convex optimisation of combined environmental economic dispatch through cultural algorithm with the consideration of the physical constraints of generating units and price penalty factors,” Africa Research Journal, SAIEE, vol. 107, no. 3, pp. 146–166, 2016.


Refbacks

  • There are currently no refbacks.


Online ISSN: 1309-0127

www.ijrer.org

ijrereditor@gmail.com; ilhcol@gmail.com;

IJRER is cited in SCOPUS, EBSCO, WEB of SCIENCE (Thomson Reuters)