基于Copula变分推断的序列不完整风电曲线定向监测方法
A Directional Monitoring Approach of Sequential Incomplete Wind Power Curves with Copula-based Variational Inference
AI总结：
针对风速波动导致不同时段功率曲线难以比较的问题，提出基于Copula变分推断的序列不完整风电曲线定向监测方法，经英国风电场数据验证，可提升建模精度与退化监测效果。
摘要：
风力涡轮机常运行于复杂工况下，易导致性能退化。精准的性能退化监测对保障风力涡轮机可靠运行、降低维护成本至关重要。风力涡轮机功率曲线监测是检测性能退化的有效途径。然而，由于风速具有间歇性和波动性，不同时段的风速范围存在差异，使得功率曲线难以比较。受此启发，我们提出了基于Copula的变分推断框架，并以此构建了序列不完整风电功率曲线估计算法。首先，基于Copula变分推断与集成样条回归模型构建单调功率曲线，同时对模型参数的先验分布进行序列更新。随后，基于名为KL散度因子的新统计量构建定向控制图，用于监测风力涡轮机的性能退化。英国东部某风电场的实测数据表明，与现有方法相比，所提方法既能提升风力涡轮机功率曲线的建模精度，又能更精准、全面地监测风力涡轮机的性能退化。
英文摘要：
Wind turbines often work under complex conditions which result in performance degradation. Accurate performance degradation monitoring is essential to ensure the reliable operation of wind turbines and reduce the maintenance costs. Wind turbine power curve monitoring is an effective way to detect performance degradation. However, due to the intermittency and fluctuation of wind speed, the wind speed range varies at different time periods, making power curves difficult to compare. Motivated by this, we proposed copula-based variational inference framework and used it to establish a sequential incomplete wind power curve estimation algorithm. First, a monotone power curve is constructed based on copula-based variational inference and integrated spline regression model. Besides, the prior distribution of model parameters are sequentially updated. Then, a directional control chart based on a new statistic named KLdivergence factor is constructed to monitor wind turbine performance degradation. The real data of a wind farm in the east of the United Kingdom shows that the proposed method can both improve the accuracy of wind turbine power curve modeling and monitor wind turbine performance degradation more precisely and comprehensively than the existing approaches.
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