随着风电的大规模发展，准确预测风电场输出功率对于风电场的选址、大规模并网及运行具有重要的作用。文中提出了局部反馈时延神经网络和全局反馈时延神经网络2种动态神经网络预测模型，以适应风功率的时间序列特性，并与静态神经网络预测模型进行了比较。以国内北方某风电场的风功率预测为例，结合气象预报数据进行提前24h的风电输出功率预测，仿真结果表明，动态神经网络在预测具有时间序列特性的风功率时效果优于静态神经网络。%The precision of wind power forecast is very important in the selection of wind farm site, and in the integration and operation of power system with increasing penetration of wind power. Compared with static neural networks, two dynamic neural network models, locally recurrent time-delay neural network model and globally recurrent time-delay neural network model, are proposed for the forecasting of a wind farm output in order to simulate the time-series characteristic of the generation series. To demonstrate the effectiveness, the models are applied and tested on a wind farm located in the north of China. Base on numerical meteorological predictions, hourly forecasts up to 24 hours ahead are produced for the wind farm. Simulation results demonstrate that the dynamic neural network models outperform the static ones in the forecast of wind power with time-series characteristic.
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