| 基于SCADA数据的风机叶片结冰检测方法 |
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| 作者姓名： | 李宁波 闫涛 李乃鹏 孔德同 刘庆超 雷亚国 |
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| 作者单位： | 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 |
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| 基金项目： | 国家自然科学基金项目(U1709208);国家自然科学基金项目(61673311);中组部"万人计划"青年拔尖人才支持计划 |
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| 摘 要： | 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。
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| 关 键 词： | 风机叶片结冰检测 SCADA数据 非主成分方向投影特征 最优阈值选择 不平衡分类 |
| 收稿时间： | 2017-12-05 |
Ice Detection Method by Using SCADA Data on Wind Turbine Blades |
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| Authors: | Ningbo LI Tao YAN Naipeng LI Detong KONG Qingchao LIU Yaguo LEI |
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| Affiliation: | 1. Shaanxi Key Laboratory of Mechanical Product Quality Assurance and Diagnostics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi Province, China;2. Huadian Electric Power Research Institute Co., LTD, Hangzhou 310030, Zhejiang Province, China |
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| Abstract: | 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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| Keywords: | ice detection on wind turbine blade SCADA data non-principal component projection feature optimal threshold selection unbalanced classification |
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