山地风受地形影响呈现强烈的间隙性、 波动性和非平稳性， 观测质量差， 常规质量控制方法无法有效地提升其观测质量。针对此， 构造一种基于变分模态分解、 卷积神经网络、 门控循环单元组合深度学习的质量控制方法（VCG）， 并引入粒子群智能优化策略、 风功率重构模型， 综合提升观测数据质量。为验证该方法的效果， 运用该方法对江西赣州、 四川广元、 安徽芜湖、 湖北黄石、 河南平顶山、 广西贺州某地共6座复杂山地风电场目标观测塔2016年10 min风速、 风向数据进行质量控制， 并与单一机器学习方法、 空间回归方法（SRT）、 反距离加权法（IDW）进行对比。结果表明， 该方法适用于山地风电场的观测风数据的质量控制， 相较于常规方法具有更高的可疑数据检错率； 控制后的数据能更好地还原观测背景场， 应用于风电场的发电量评估业务具有更低的误差率； 且具有地形适应性强的特点。

Mountainous winds exhibit strong intermittent， fluctuating， and non-stationary characteristics due to the influence of terrain， resulting in poor observation quality， which makes conventional quality control methods unable to effectively improve their observation quality.To address this issue， a quality control method （VCG） based on variational mode decomposition， convolutional neural networks， and deep learning of gated cyclic units is constructed， and a particle swarm optimization strategy and wind power reconstruction model are introduced to comprehensively improve the quality of observation data.To verify the effectiveness of this method， 10 minute wind speed and direction data of target wind turbines in six complex mountainous wind farms in Jiangxi Ganzhou， Sichuan Guangyuan， Anhui Wuhu， Hubei Huangshi， Henan Pingdingshan， and Guangxi Hezhou in 2016 was quality controlled by VCG and compared with single machine learning method， spatial regression method （SRT）， and inverse distance weighting method （IDW）.The results indicate that VCG method is suitable for quality control of observed wind data in mountainous wind farms， and has a higher error detection rate for suspicious data compared to conventional methods； The controlled data can better restore the observed background field and have a lower error rate when applied to the power generation evaluation business of wind farms； And it has the characteristics of strong terrain adaptability.