The trend towards larger wind turbines and remote locations of wind farms fuels the demand for automated condition monitoring strategies that can reduce the operating cost and avoid unplanned downtime. Normal behaviour modelling has been introduced to detect anomalous deviations from normal operation based on the turbine's SCADA data. A growing number of machine learning models of the normal behaviour of turbine subsystems are being developed by wind farm managers to this end. However, these models need to be kept track of, be maintained and require frequent updates. This research explores multi-target models as a new approach to capturing a wind turbine's normal behaviour. We present an overview of multi-target regression methods, motivate their application and benefits in wind turbine condition monitoring, and assess their performance in a wind farm case study. We find that multi-target models are advantageous in comparison to single-target modelling in that they can reduce the cost and effort of practical condition monitoring without compromising on the accuracy. We also outline some areas of future research.
翻译:风力发电机和风力农场偏远地点的趋势刺激了对自动状况监测战略的需求,这种战略可以降低运营成本,避免意外停机时间。根据涡轮机的SCADA数据,采用了正常行为模型来检测与正常运行异常的偏差。风力发电机经理正在为此开发越来越多的涡轮子正常行为的机器学习模型。然而,这些模型需要不断跟踪、保持和经常更新。这项研究探索多目标模型,作为捕捉风力涡轮机正常行为的一种新方法。我们概述了多目标回归方法,在风力涡轮机状况监测中鼓励应用这些方法并从中受益,并在风力发电机状况案例研究中评估这些方法的性能。我们发现,多目标模型与单一目标模型相比是有利的,因为它们可以在不影响准确性的情况下降低实际状况监测的成本和努力。我们还概述了未来研究的一些领域。