论文标题
多目标正常行为模型用于风电场状态监测
Multi-target normal behaviour models for wind farm condition monitoring
论文作者
论文摘要
较大的风力涡轮机和风电场偏远位置的趋势促进了对自动状况监测策略的需求，这些策略可以降低运营成本并避免计划外的停机时间。已经引入了正常行为模型，以根据涡轮机的SCADA数据检测正常操作的异常偏差。风电场经理正在开发越来越多的涡轮体子系统正常行为的机器学习模型。但是，需要跟踪，维护并需要频繁更新。这项研究探索了多目标模型，作为捕获风力涡轮机正常行为的一种新方法。我们介绍了多目标回归方法的概述，激励其在风力涡轮机构监测中的应用和益处，并在风电场案例研究中评估其性能。我们发现，与单目标建模相比，多目标模型是有利的，因为它们可以降低实际条件监测的成本和精力而不会损害准确性。我们还概述了未来研究的一些领域。
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.
