引用本文: | 樊 帅,唐群先.基于AdaBoost-SAMME的风力发电机组变桨异常识别系统[J].电力系统保护与控制,2020,48(21):31-40.[点击复制] |
FAN Shuai,TANG Qunxian.Wind turbine pitch anomaly recognition system based on AdaBoost-SAMME[J].Power System Protection and Control,2020,48(21):31-40[点击复制] |
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摘要: |
为了实现对风力发电机组变桨系统的预见性维护,提出了在机组健康管理平台上开发变桨异常识别系统的设计思路。在模型算法构建过程中,提出将变桨速度的频域特征作为分类特征,实现了对异常征兆的精确刻画。利用AdaBoost-SAMME算法将变桨速度分为高频、低频以及正常三类,与人工神经网络、支持向量机、随机森林等五种算法对比发现,即使各类样本数量严重不均衡,AdaBoost-SAMME算法的准确率、查准率、查全率及G-mean等评价指标也都优于其他算法。为了提高系统的自学习能力,提出了一种基于欧式距离的新样本判断方法,以此自动扩增训练样本规模。应用实例验证表明,基于AdaBoost-SAMME算法的变桨异常识别系统具有显著的分类效果和良好的稳定性,解决了不同机型的变桨速度频域特征普适性规律不明显,常规逻辑判断方法无法识别变桨系统异常的技术难题。该系统实现了在故障前识别出异常的预警功能,能够指导现场人员开展预见性维护,提高机组的可靠性和可利用率。 |
关键词: 风力发电机组 变桨系统 异常识别 AdaBoost-SAMME 特征选择 |
DOI:DOI: 10.19783/j.cnki.pspc.191560 |
修订日期:2020-04-25 |
基金项目:国家重点研发计划项目资助(2016YFB1000705) |
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Wind turbine pitch anomaly recognition system based on AdaBoost-SAMME |
FAN Shuai,TANG Qunxian |
(1. Beijing Goldwind Science & Creation Windpower Equipment Co., Ltd., Beijing 100176, China;
2. University of International Business and Economics, Beijing 100029, China) |
Abstract: |
To realize predictive maintenance of the pitch system in wind turbines, a design idea is presented whereby a pitch anomaly recognition system can be developed on a wind turbines health management platform. In constructing the model algorithm, the frequency domain characteristics of pitch velocity are suggested as classification features, and precise characterization of abnormal symptoms is achieved. The pitch velocity is divided into three categories: high, low and normal frequency with AdaBoost-SAMME as a classification algorithm. Comparing with five algorithms including an artificial neural network, a support vector machine and a random forest, it is found that the AdaBoost-SAMME algorithm is superior in accuracy, precision, recall and G-mean index although a serious disproportion exists in the number of samples. In order to improve the self-learning ability of the recognition system, a new sample judgment method based on Euclidean distance is proposed to automatically expand the training sample size. The application example demonstrates that the pitch anomaly recognition system using the AdaBoost-SAMME algorithm possesses noticeable classifying quality and stability. It solves the technical problem that for the general adaptive law of pitch velocity frequency domain characteristics in different models is implicit, and a conventional logic judgment method cannot be used to identify a pitch system abnormality. The system achieves the early warning function of identifying anomalies before faults. This can guide field personnel to practice predictive maintenance and improve the reliability and utilization rate of wind turbines.
This work is supported by National Key Research and Development Program of China (No. 2016YFB1000705). |
Key words: wind turbines pitch system anomaly recognition AdaBoost-SAMME feature selection |