基于小波变换和神经网络的短期风电功率预测方法

随着并网风电场规模的不断增大，为保证电力系统运行的稳定性、合理制定调度计划、提高风电场在发电市场的竞争力，需要对短期风电功率进行准确地预测。该文提出一种小波变换和神经网络理论相结合的综合预测方法，将历史风电功率序列和历史风速序列分别进行小波单尺度分解，得到对应的概貌功率、细节功率和概貌风速、细节风速；然后用概貌功率和概貌风速序列训练BP神经网络，预测未来的概貌功率；用细节功率和细节风速序列训练BP神经网络，预测未来的细节功率。在此基础上，将概貌功率和细节功率叠加，得到最终预测结果。对我国某风电场的实际数据

A method of short-term wind power forecast based on wavelet transform and neural network

With the increasing scale of grid connected wind forms, it is important to predict the wind power in order to ensure stability of the power system, and make a reasonable dispatching scheme, and improve the wind form competitiveness in generation market. A novel method was proposed and applied to forecast the short-term wind power in this paper. Wavelet transforms and neural networks were combined in this method. First, the history wind speed and history wind power was decomposed by multi-resolution analysis. Then, the general signals and detail signals of wind power were forecasted by neural networks separately, which introduced the general signals and detail signals of wind speed as the effect factors. Finally, the general wind power and detail wind power were reproduced to obtain the forecasting wind power. The validity and feasibility of the method were verified through the actual data from a wind farm in China.