Abstract：Wind energy resource assessment is a key step of wind farm planning and design. A hybrid neural network (HNN) based wind energy resource assessment method is proposed to improve the assessment accuracy, and the method allows the use of regional information. Firstly, the HNN based correlate model is developed according to the concurrent wind speeds of the reference weather stations and the candidate wind farm. In order to obtain wider learning capability and avoid being trapped in a local minimum, the different training algorithm and the adaptive particle swarm optimization (APSO) are used in the HNN. Then, the whole long-term wind speed and direction data are applied to the model, thus the long-term wind characteristics of the candidate wind farm are obtained. The wind energy resource assessment parameters are subsequently computed on the basis of the knowledge of these wind speeds. The simulation results show that the proposed method has relatively high accuracy.
王娜,周有庆,邵霞. 基于混合神经网络的风电场风资源评估[J]. 电工技术学报, 2015, 30(14): 370-376.
Wang Na,Zhou Youqing,Shao Xia. Wind Energy Resource Assessment of Wind Farm Based on Hybrid Neural Network. Transactions of China Electrotechnical Society, 2015, 30(14): 370-376.
[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.
2015, Vol. 30 
