风力涡轮机诊断中的机器学习技术

Uriel A. García, Pablo H. Ibargüengoytia, Lorena Díaz González, Jorge Hermosillo Valadez
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引用次数: 0

摘要

墨西哥风能创新中心(CEMIE-Eólico)利用监控和数据采集系统(SCADA)的信号,设计了一个基于涡轮机行为模型的风力涡轮机诊断系统。系统提供了在出现故障时显示异常行为的变量模式。这些模式是通过在故障显示的时间窗口内检测变量的异常行为而形成的。本文介绍了将机器学习技术应用于风电机组故障诊断系统后的故障识别。训练和验证数据是通过使用国家电力和清洁能源研究所(INEEL)设计的墨西哥风力机(MEM)对风力涡轮机的六种不同故障进行模拟获得的。应用该诊断系统,生成了异常行为剖面图,并进行了基于“随机森林”算法的故障模式多类分类实验。最后,使用准确度和精密度指标对算法性能进行评估,在识别根故障的模式分类中达到91%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Técnicas de aprendizaje automático en el diagnóstico de aerogeneradores
The Mexican Center for Innovation in Wind Energy (CEMIE-Eólico) designed a wind turbine diagnostic system based on turbine behavior models using the signals of the Supervisory Control and Data Acquisition system (SCADA). The system provides a pattern of variables that exhibit abnormal behavior in the presence of a fault. The patterns are formed with the detection of the abnormal behavior of the variables during a time window in which the failure manifests itself. This paper presents the application of machine learning techniques for the identification of faults in wind turbines after the diagnostic system. The training and validation data were obtained from the simulation of six different faults in the wind turbine using the Mexican Wind Machine (MEM) designed at the National Institute of Electricity and Clean Energy (INEEL). The diagnostic system was applied, profiles of abnormal behavior were generated and experiments were carried out for the multiclass classification of fault patterns using the "Random Forest" algorithm. Finally, the algorithm performance was evaluated using accuracy and precision metrics achieving 91% in the classification of patterns to identify the root failure.
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