Offshore wind structures are subject to deterioration mechanisms throughout their operational lifetime. Even if the deterioration evolution of structural elements can be estimated through physics-based deterioration models, the uncertainties involved in the process hurdle the selection of lifecycle management decisions. In this scenario, the collection of relevant information through an efficient monitoring system enables the reduction of uncertainties, ultimately driving more optimal lifecycle decisions. However, a full monitoring instrumentation implemented on all wind turbines in a farm might become unfeasible due to practical and economical constraints. Besides, certain load monitoring systems often become defective after a few years of marine environment exposure. Addressing the aforementioned concerns, a farm-wide virtual load monitoring scheme directed by a fleet-leader wind turbine offers an attractive solution. Fetched with data retrieved from a fully-instrumented wind turbine, a model can be trained and then deployed, thus yielding load predictions of non-fully monitored wind turbines, from which only standard data remains available. In this paper, we propose a virtual load monitoring framework formulated via Bayesian neural networks (BNNs) and we provide relevant implementation details needed for the construction, training, and deployment of BNN data-based virtual monitoring models. As opposed to their deterministic counterparts, BNNs intrinsically announce the uncertainties associated with generated load predictions and allow to detect inaccurate load estimations generated for non-fully monitored wind turbines. The proposed virtual load monitoring is thoroughly tested through an experimental campaign in an operational offshore wind farm and the results demonstrate the effectiveness of BNN models for fleet-leader-based farm-wide virtual monitoring.
翻译:尽管通过物理降解模型可以估计结构要素的恶化演变,但整个过程所涉及的不确定性阻碍了生命周期管理决定的选择。在这一假设中,通过高效监测系统收集相关信息可以减少不确定性,最终推动更理想的生命周期决定。然而,由于实际和经济上的限制,对农场中所有风力涡轮机实施的全面监测仪器可能变得不可行。此外,某些负荷监测系统在海洋环境暴露几年后往往出现缺陷。针对上述关切,由车队领导虚拟风力涡轮公司指导的全农场范围的虚拟负载监测计划提供了一个有吸引力的解决方案。利用从全设备型风力涡轮公司获取的数据,可以培训并随后部署一个模型,从而得出未经充分监测的风力涡轮机的负荷预测,而仅从中可以提供标准数据。我们提议通过Byesian神经网络(BNNS)开发一个虚拟负荷监测模型,我们提供建造、培训和部署BNNN型机队虚拟载量虚拟监测计划所需的相关实施细节。通过不精确的机载量监测模型,对BNNN型机载机轮进行不完全的机载量监测,对机轮机载量进行不精确的虚拟预测,对B进行不进行不进行不精确的模拟的模拟的模拟的模拟模拟的模拟的模拟预测。