【摘要】 复杂山地风电场普遍存在观测风速数据质量差引起风资源评估误差大、风功率预测精度低的问题。而复杂山地风速呈现较强的间隙性、波动性和非平稳性，导致常规质量控制方法无法有效提高数据质量。针对此，提出一种基于粒子群改进变分模态分解和长短期记忆网络的集成学习算法（PVL），并应用于复杂山地观测风速的质量控制以提高风速数据的质量。以广西某复杂山地风场内5基观测塔2015—2016年逐10 min风速数据为案例进行PVL应用效果检验，并与传统单站及空间回归法、反距离加权法进行对比。应用表明，PVL比传统方法具有更高的寻误率，且在异地形、多风况上具有更强的适应性。更多还原

【Abstract】 There are many problems in complex mountain wind farms,such as large errors of wind resource evaluation and low accuracy of wind power prediction caused by poor quality of observed wind speed data. Because of the strong intermittent,fluctuating,and nonstationary characteristics presented by the wind speed in complex mountain wind farms,conventional quality control methods cannot effectively improve data quality. For this situation,an integrated learning algorithm(PVL)based on particle swarm optimization improved variational modal decomposition improved by particle swarm optimization and long short-term memory is proposed and applied to the quality control of wind speed observations in complex mountainous areas to improve the quality of wind speed data. In order to assess the feasibility and applicability of the proposed method,the 10 minutes wind speed observed in five observation tower of a complex mountain wind farm in Guangxi from 2015 to 2016 were examined. Otherwise,we compared this method to spatial regression test(SRT)and inverse distance weighting method(IDW). The results show that the method can more effectively flag suspicious data,and it also has the advantages of high identification accuracy,strong adaptability to different terrains and wind conditions.更多还原