刘军, 魏敏, 陈克锐, et al. Frequent Yawing Detection for Wind Turbine based on Outlier Detecting Method[J]. 2020, (3): 52-58. DOI: 10.16056/j.2096-7705.2020.03.012.
基于离群点检测的风电机组偏航频繁检测
摘要
基于离群点检测方法
对风电机的偏航运行数据进行分析
统计风电机组的偏航次数
对风场内每台风电机组的偏航次数进行离群标注
并计算一定时间窗口内
每台风电机组偏航频繁的概率
并判定其是否存在偏航频繁。研究计算结果表明
采用此检测算法
可以有效快速高效检测在一定的时间期内风电场内的风电机组是否存频繁偏航的问题
并及时预警反馈给现场
可以为风电机组的智慧运维提供有力的技术支持。
Abstract
Based on the outlier detection method
the operating data of yawing wad analyzed. The yawing times was counted.And the yawing times of each wind turbine in wind farm would be regard as outlier or not. And the probability of frequent yawing in one fixed time region would be calculated. The wind turbine would be regarded as anomaly by the probability of frequent yawing. The result shown that
the outlier detection method could be used to detect whether there was frequent yawing anomaly of the wind turbine in wind farm or not. And the result could be sent to site quickly if there was frequent yawing anomaly
which could give useful and strong technical support for the smart operation for wind turbines.