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风电机组出力可由风速计算得出,提高风速预测精度对减小风电并网冲击、合理调度风能资源至关重要。基于风电场气象及风速数据的时间连续性,提出了一种加入误差实时校正环节及风速变化趋势分析的改进方法介绍,在提高风速预测精度的同时有效改善了过校正情况。采用某个风电场的实际运行数据进行了仿真,结果表明,所提出的改进BP神经网络风速预测模型方法具有较好的预测精度。
Wind turbine output can be calculated from the wind speed, to improve the accuracy of wind speed to reduce wind power grid impact and rational scheduling of wind energy resources is essential. Based on the time continuity of meteorological and wind speed data in wind farms, an introduction to an improved method of adding errors to real-time calibration and wind speed trend analysis is proposed. The improvement of wind speed prediction accuracy and the overcorrection are effectively improved. The actual operation data of a wind farm is used to simulate the wind farm. The results show that the proposed BP neural network wind speed prediction model has good prediction accuracy.