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STMAS-WRF三维风速预报产品在沿海风电场的适用性分析

Analysis on the Applicability of STMAS-WRF Three-Dimensional Wind Speed Forecast Product in Coastal Wind Farms

  • 摘要:
    目的 随着“十四五”能源规划持续推进,我国风电产业已进入大规模、高比例发展的新阶段。对于风电场运营效率而言,提高风机风速预报的准确率至关重要。
    方法 文章以沿海某风电场为代表,对STMAS-WRF模式三维风速预报产品的预报效果开展多尺度、全方位的检验评估。
    结果 研究表明:(1)风机高度预报风速和实测风速趋势总体一致,预报风速整体较实测风速偏大。预报和实测风速存在较好的相关性,但随着预报时效的增加,准确率逐渐降低;(2)风机日、月平均风速预报趋势与实测风速一致,月平均风速预报偏差8月最小,6月次之;10月和11月较大,说明秋季风速预报难度较大;(3)变化风速段和小风速段预报效果较额定风速段好,说明模式对于极值的预报能力存在不足;(4)A、B区风机预报效果较C、D区好,可能与C、D区地势更高有关,预报难度和复杂度增大。
    结论 STMAS-WRF模式预报效果较好,且误差分布存在一定的规律性,下一步可对该产品进行系统性订正,提高风速预报准确率,服务于"双碳"目标下的风力资源评估及风电场风功率预报。

     

    Abstract:
    Objective With the continuous advancement of the "14th Five-Year Plan" for energy, China's wind power industry has entered a new stage of large-scale and high-proportion development. For the operational efficiency of wind farms, improving the accuracy of wind speed forecasts for wind turbines is of vital importance.
    Method This paper took the coastal wind farm as a representative to conduct an applicability study on the application effect of the three-dimensional wind speed forecast product of the STMAS-WRF forecast model.
    Result The research shows that: (1) The trends of the predicted wind speed and the measured wind speed at the height of the wind turbine are generally consistent, and the predicted wind speed is generally larger than the measured wind speed. There is a good correlation between the forecast and the measured wind speed, but as the forecast period increases, the accuracy gradually decreases. (2) The forecast trends of the daily and monthly average wind speeds of the wind turbines are consistent with the measured wind speeds. The deviation of the monthly average wind speed forecast is the smallest in August and the second smallest in June. The wind speed is relatively high in November and October, indicating that it is more difficult to predict wind speed in autumn. (3) The forecast effect of the variable wind speed range and the low wind speed range is better than that of the rated wind speed range, indicating that the model's prediction ability for extreme values is insufficient. (4) The prediction effect of wind turbines in areas A and B is better than that in areas C and D, which may be related to the higher terrain in Areas C and D, increasing the difficulty and complexity of prediction.
    Conclusion The prediction performance of the STMAS-WRF model is relatively good, and the error distribution has certain regularity. In the next step, this product can be corrected to improve the accuracy of wind speed prediction.

     

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