A Comparative Analysis on the Variability of Temperature Thresholds through Time for Wind Turbine Generators Using Normal Behaviour Modelling
A Comparative Analysis on the Variability of Temperature Thresholds through Time for Wind Turbine Generators Using Normal Behaviour Modelling
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DOI：
10.3390/en15145298
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发表时间：
2022-07
期刊：
影响因子：
3.2
通讯作者：
A. Turnbull;James R Carroll;A. McDonald
中科院分区：
文献类型：
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作者：
A. Turnbull;James R Carroll;A. McDonald
在过去几年中，数据驱动的正常行为模型作为一种方便的涡轮机运行状况建模方法以检测异常情况而获得了广泛关注。通过利用高维操作关系，可以基于每个单独的涡轮机唯一的操作包络线自动计算温度阈值，从而在理论上最小化错误警报并提供更可靠的诊断。这项工作的目的是提供进一步的洞察，在实践中实施正常行为温度模型的实际用途和局限性，告知从业人员，以及协助改善风力涡轮机发电机故障检测系统。结果表明，平均而言，只要两个月的数据就足以产生稳定的温度报警阈值，最坏情况下的例子需要大约200-290天的数据，这取决于组件和所需的收敛标准。
Data-driven normal behaviour models have gained traction over the last few years as a convenient way of modelling turbine operational health to detect anomalies. By leveraging high-dimensional operational relationships, temperature thresholds can be automatically calculated based on each individual turbine unique operating envelope, in theory minimising false alarms and providing more reliable diagnostics. The aim of this work is to provide further insight into practical uses and limitations of implementing normal behaviour temperature models in practice, to inform practitioners, as well as assist in improving wind turbine generator fault detection systems. Results suggest that, on average, as little as two months of data are adequate to produce stable temperature alarm thresholds, with the worst case example requiring approximately 200–290 days of data depending on the component and desired convergence criteria.