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基于混合神经网络的风电场风资源评估

, PP. 370-376

Keywords: 风电场,风资源评估,混合神经网络,自适应粒子群优化

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Abstract:

准确的风资源评估是风电场规划和设计的前提。为了提高风电场风资源评估的精度,提出了一种基于混合神经网络的风电场风资源评估方法,该方法可综合利用风电场附近区域信息进行评估。首先根据风电场和附近参考气象站的同期数据建立基于混合神经网络的相关模型,训练得到神经网络的权值参数,为了提高神经网络的学习能力和避免陷入局部最优,混合神经网络采用不同的训练方法,并且采用自适应粒子群算法进行优化;再将参考气象站的历史观测数据应用到该模型中,即可得到风电场的长期风速特性,在此基础上进行风资源评估参数的计算。仿真结果表明该方法具有较高的精度。

References

[1]  José A. Carta, Sergio Velázquez, J.M. Matías. Use of Bayesian networks classifiers for long-term mean wind turbine energy output estimation at a potential wind energy conversion site[J]. Energy conversion and management, 2011, 52(2): 1137-1149.
[2]  Sergio Velázquez, José A. Carta, Matí.as J M. Com- parison between ANNs and linear MCP algorithms in the long-term estimation of the cost per kW h produced by a wind turbine at a candidate site: A case study in the Canary Islands. Applied Energy, 2011, 88(5): 3869-3881.
[3]  José A. Carta, Sergio Velázquez. A new probabilistic method to estimate the long-term wind speed characteristics at a potential wind energy conversion site[J]. Energy. 2011, 36(5): 2671-2685.
[4]  Alejandro Romo Perea, Javier Amezcua, Oliver Probst. Validation of three new measure-correlate- predict models for the long-term prospection of the wind resource[J]. Journal of Renewable and Sustainable Energy, 2011, 3(2): 023105-1-20.
[5]  Anthony L R, John W R, James F.M. Comparison of the performance of four measure-correlate-predict algorithm[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2005, 93(3): 243-264.
[6]  Saavedra-Moreno B, Salcedo-Sanz S, Carro-Calvo L. et al. Very fast training neural-computation techniques for real measure-correlate-predict wind operators in wind farms[J]. Journal of Wind Engineering and industrial Aerodynamics, 2013, 116(3): 49-60.
[7]  Mabel M C, Fernandez. Estimation of energy yield from wind farms using artificial neural networks. IEEE Transcations on Energy Conversion, 2009, 24(2): 459-464.
[8]  Sergio Velázquez, José A. Carta, Matí.as J M. Influence of the input layer signals of ANNs on wind power estimation for a target site: A case study[J]. Renewable and Sustainable Energy Reviews, 2011, 15: 1556-1566.
[9]  Zhang J, Chowdhury S, Messac A. Assessing long- term wind conditions by combining different measure- correlate-predict algorithms[C]. ASME 2013 Interna- tional Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Portland, Oregon, August 4-7, 2013: 1-9.
[10]  Schemaiah Matthias Weekes, Alison S. Tomlin. Low- cost wind resource assessment for small-scale turbine installations using site pre-screening and short term wind measurements[J]. IET Renewable Power Genera- tion, 2014, 8(4): 348-358.
[11]  Ali Dinler. A new low-correlation MCP (measurement- correlate-predict) method for wind energy forecasting [J]. Energy, 2013, 63(10): 152-160.
[12]  杨振斌, 朱瑞兆, 薛桁. 风电场风能资源评价两个新参数[J]. 太阳能学报, 2007, 28(3): 248-251. Yang Zhenbin, Zhu Ruizhao, Xue Hengl. Two new concepts on wind energy assessment in wind farm [J]. Acta Energiae Solaris Sinica, 2007, 28(3): 248-251.
[13]  GB/T 18710-2002, 风电场风能资源评估方法.
[14]  Amjady N, Daraeepour A, Keynia F. Day-ahead elec- tricity price forecasting by modified relief algrorith and hybrid neural network[J]. IET Generation, Trans- mission & Distribution, 2010, 4(3): 432-444.
[15]  Zhang Z H, Zhang J, Li Y. Adaptive particle swarm optimaization[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 2009, 39(6): 1362-1381.

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