风力发电机组隐性运行状态第一阶段分析
Phase I analysis of hidden operating status for wind turbine
摘要：
基于监控与数据采集（SCADA）的数据驱动方法成为风力发电机组状态监测的新趋势。然而，由于低采样频率和复杂的涡轮机工作动态，SCADA数据已知是低质量的。在这项工作中，我们专注于SCADA数据的第一阶段分析，以更好地了解涡轮机的运行状态。功率曲线是衡量系统性能的重要指标之一。在通过精确的功率曲线模型调整发电量后，应用功能强大的无分布控制图，该模型明确考虑了可能影响涡轮机性能的已知因素。在实际的实地案例研究中发现了信息量很大的失控段。第一阶段的分析可以帮助提高风力涡轮机的监测、可靠性和维护，从而实现更智能的风能系统。
英文摘要：
Data-driven methods based on Supervisory Control and Data Acquisition (SCADA) become a recent trend for wind turbine condition monitoring. However, SCADA data are known to be of low quality due to low sampling frequency and complex turbine working dynamics. In this work, we focus on the phase I analysis of SCADA data to better understand turbines' operating status. As one of the most important characterization, the power curve is used as a benchmark to represent normal performance. A powerful distribution-free control chart is applied after the power generation is adjusted by an accurate power curve model, which explicitly takes into account the known factors that can affect turbines' performance. Informative out-of-control segments have been revealed in real field case studies. This phase I analysis can help improve wind turbine's monitoring, reliability, and maintenance for a smarter wind energy system.
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