数据驱动的风电机组叶片结冰在线检测方法
摘要:随着风力发电机组的发展，低温潮湿环境下的叶片结冰问题受到了越来越多的重视。叶片结冰会导致风力发电机组功率损耗，严重时还会诱发叶片的断裂，造成巨大的发电损失和维修成本。目前风力发电机组发出结冰警报时往往结冰情况已经非常严重，因此实现对叶片结冰早期的预测是十分有必要的。采用数据驱动方法来对风力发电机组叶片结冰进行预测，通过对原始数据进行数据预处理、特征提取、构建模型、训练模型等步骤来辨别叶片状态；分别构建了逻辑回归模型和XGBoost模型，分析比较上述2个模型对于叶片结冰预测的准确率和预测时间长短，得出适用于叶片结冰在线检测的模型。经计算分析，XGBoost模型在叶片结冰预测问题上的效果优于逻辑回归模型，同时预测时间仅为0.449 s，完全可以达到在线检测的目的。
Abstract：With the development of the wind turbine, the icing problem of wind turbine blades under low temperature and humidity has been paid more and more attention. When the blades freeze, it causes the power loss of the wind turbine, causing the blades to break up in severe time, resulting in huge power loss and maintenance costs. At present, when the wind turbine issues an alarm about icing, the ice conditions are usually very serious. So it is necessary to achieve the early prediction of icing about wind turbine blades. In this paper, data driven method is used to predict the blade icing of wind turbine, the blade states are identified by initial data preprocessing, feature extraction, model construction and training. Logical Regression model and XGBoost model are constructed respectively. Then, the accuracy and prediction time of the above two models for blade icing prediction are analyzed and compared. Finally, the model suitable for online detection of blade icing is obtained. Through calculation and analysis, the XGBoost model is better than the Logistic Regression model in predicting blade icing, and the prediction time is only 0.449 seconds, which can fully achieve the purpose of online detection.
标题：数据驱动的风电机组叶片结冰在线检测方法
title:On-Line Detection Method of Ice on Wind Turbine Blade Driven by Data
作者：郑若楠
authors:ZHENG Ruonan
关键词：风力发电机组,叶片结冰,数据驱动,逻辑回归模型,XGBoost模型,
keywords:wind turbine,blade icing,data-driven,Logistic Regression model,XGBoost model,
发表日期：2019-03-21
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