1.西安交通大学陕西省机械产品质量保障与诊断重点实验室, 陕西省 西安市 710049
2.华电电力科学研究院有限公司, 浙江省 杭州市 310030
[ "李宁波(1992), 男, 博士研究生, 主要研究方向为机械设备剩余寿命预测, liningbo1992@163.com" ]
收稿:2017-12-05,
纸质出版:2018-02-28
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李宁波, 闫涛, 李乃鹏, 等. 基于SCADA数据的风机叶片结冰检测方法[J]. 发电技术, 2018,39(1):58-62.
Ningbo LI, Tao YAN, Naipeng LI, et al. Ice Detection Method by Using SCADA Data on Wind Turbine Blades[J]. Power Generation Technology, 2018, 39(1): 58-62.
李宁波, 闫涛, 李乃鹏, 等. 基于SCADA数据的风机叶片结冰检测方法[J]. 发电技术, 2018,39(1):58-62. DOI: 10.12096/j.2096-4528.pgt.2018.010.
Ningbo LI, Tao YAN, Naipeng LI, et al. Ice Detection Method by Using SCADA Data on Wind Turbine Blades[J]. Power Generation Technology, 2018, 39(1): 58-62. DOI: 10.12096/j.2096-4528.pgt.2018.010.
针对工作在寒冷地区的风机易出现的叶片结冰现象,提出一种基于SCADA数据的风机叶片结冰检测方法。根据叶片结冰会增大发电机的功率损耗,选择风速与网侧有功功率2个变量,利用主成分分析技术构造对叶片结冰敏感的风速与网侧有功功率在非主成分方向投影特征,通过选择最优阈值使逻辑回归分类器适用于不平衡分类,可以实现风机叶片结冰检测自动化与智能化。通过中国工业大数据创新竞赛数据验证了该方法的有效性。
Aimed at the phenomenon of wind turbine blade icing
which is easy to occur in the cold areas
a method of icing detection of wind turbine blades using SCADA data was proposed. When the blades are icing
the power loss of generator will be increased
thus the method picks two variables
wind speed and power. Principal component analysis (PCA) was used to construct the projection feature on non-principal component direction which is sensitive to icing and active power of network. By choosing the optimal threshold
the logistic regression classifier is suitable for unbalanced classification. The effectiveness of this method was verified by the data of China Industrial Big Data Innovation Competition.
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Kim D, Sampath U, Kim H, et al. A fiber-optic ice detection system for large-scale wind turbine blades[C]. Proceedings of Optical Modeling and Performance Predictions Ⅸ. International Society for Optics and Photonics, 2017, 10374: 103740B.
Shi Q. Model-based detection for ice on wind turbine blades[D]. Trondheim: NTNU, 2017. http://hdl.handle.net/11250/2450068 .
梁颖, 方瑞明.基于SCADA和支持向量回归的风电机组状态在线评估方法[J].电力系统自动化, 2013, 37(14):7-12.
风机运行脱敏数数据[EB/OL ] . [2017-07-15 ] [2018-02-28 ] . http://www.industrial-bigdata.com http://www.industrial-bigdata.com .
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