%0 Journal Article %1 Chen2013Statistical %A Chen, K. %A Song, M. X. %A Zhang, X. %D 2013 %J Journal of Wind Engineering and Industrial Aerodynamics %K windfarm energy renewables wind %P 69--77 %R 10.1016/j.jweia.2013.05.015 %T A statistical method to merge wind cases for wind power assessment of wind farm %U http://dx.doi.org/10.1016/j.jweia.2013.05.015 %V 119 %X A statistical model is proposed to merge wind cases for wind power assessment. The wind turbine power curve is modeled by power law or section power law. The error of energy assessment by the statistical method is less than 0.1\%. The computational time for wind turbine positioning optimization is reduced. Wind power assessment of wind farm is a critical stage in wind energy utilization. This paper presents a statistical method to model the wind speed distribution for wind power assessment of wind farm. The number of wind cases is reduced and the wind rose is simplified through merging the wind speeds. This method is applied to wind power assessment combined with the linear wake model and the wind turbine power curve, and also can be used in wind turbine positioning optimization. The real coding genetic algorithm is employed to evaluate the performance of the statistical method for wind turbine positioning optimization problem. Two numerical cases are used to test the method. The results show that the proposed method can maintain the accuracy of the wind power assessment and reduce the computational time. This statistical method can effectively accelerate the process of wind power assessment and wind turbine positioning optimization in wind farm.

@article{Chen2013Statistical, abstract = { A statistical model is proposed to merge wind cases for wind power assessment. The wind turbine power curve is modeled by power law or section power law. The error of energy assessment by the statistical method is less than 0.1\%. The computational time for wind turbine positioning optimization is reduced. Wind power assessment of wind farm is a critical stage in wind energy utilization. This paper presents a statistical method to model the wind speed distribution for wind power assessment of wind farm. The number of wind cases is reduced and the wind rose is simplified through merging the wind speeds. This method is applied to wind power assessment combined with the linear wake model and the wind turbine power curve, and also can be used in wind turbine positioning optimization. The real coding genetic algorithm is employed to evaluate the performance of the statistical method for wind turbine positioning optimization problem. Two numerical cases are used to test the method. The results show that the proposed method can maintain the accuracy of the wind power assessment and reduce the computational time. This statistical method can effectively accelerate the process of wind power assessment and wind turbine positioning optimization in wind farm.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Chen, K. and Song, M. X. and Zhang, X.}, biburl = {https://www.bibsonomy.org/bibtex/218ce77adfe6a145a1cf363a11edd2eb2/pbett}, citeulike-article-id = {12527637}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.jweia.2013.05.015}, comment = {(private-note)(MO has access to this journal!) Has a couple of wind turbine power curve models, including quadratic as well as cubic.}, doi = {10.1016/j.jweia.2013.05.015}, interhash = {760c4718f38e21f6b892d35ed9d24d66}, intrahash = {18ce77adfe6a145a1cf363a11edd2eb2}, issn = {01676105}, journal = {Journal of Wind Engineering and Industrial Aerodynamics}, keywords = {windfarm energy renewables wind}, month = aug, pages = {69--77}, posted-at = {2013-07-25 11:42:33}, priority = {2}, timestamp = {2018-06-22T18:33:28.000+0200}, title = {A statistical method to merge wind cases for wind power assessment of wind farm}, url = {http://dx.doi.org/10.1016/j.jweia.2013.05.015}, volume = 119, year = 2013 }