Development of an integrated modeling framework for wind turbine health condition assessment

开发风力涡轮机健康状况评估的集成建模框架

• 批准号: RGPIN-2017-04143
• 负责人: Sun, Qiao
• 金额: $ 2.26万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710901
• 关键词: Development; integrated; modeling; framework; wind

Design and operation of wind power stations are guided by the “L3 conditions”, namely, low cost, long-lasting, and low service requirement. Not only do wind turbine components fail at higher rates because of their extreme operating conditions, the costs associated with the failure are also higher than other machinery systems due to the need for special equipment such as a large crane. Furthermore, wind turbines may not be accessible at times of extreme weather conditions, causing extended shutdown periods and loss of production. A reliable and automated machine health condition monitoring system can enable effective predictive maintenance practice and contribute directly to all three L's. Such a system should have the ability to detect faults, link them to root causes, and predict machine health state at future times. In the general area of machine condition monitoring, solutions in fault diagnosis are becoming increasingly sophisticated with improved reliability in fault classification and robustness to operating and environmental condition variations. However, very few solutions exist that can determine root causes of faults or predict failure at the system level. These are the main reasons for the slow industry adoption of research and development results because mere detection of faults without actionable information, such as when and where failure will occur, does not necessarily translate to cost savings. A system level model that includes component models based on underlying physical principles can provide the key to a solution with the much needed fault prediction capabilities. In this research program, we aim to establish an integrated wind turbine system model that can represent a digital copy of the actual system particularly in terms of its health condition. At any point of time, this model can indicate fault location and severity. It can be used to simulate the system's behavior as the result of fault progression in future times to enable failure prediction. It can also be used to analyze the effect of a localized fault on system's overall dynamics behavior and the well-being of other components to inform effective intervention. The successful delivery of the research outcome will represent a leap forward toward building an effective predictive maintenance solution for the wind energy and other industries.

风力发电站的设计和运行遵循“L3条件”，即低成本、持久和低服务要求。 风力涡轮机部件不仅由于其极端的操作条件而以更高的比率发生故障，而且由于需要诸如大型起重机的专用设备，与故障相关联的成本也高于其它机械系统。 此外，风力涡轮机在极端天气条件下可能无法接近，导致停机时间延长和生产损失。一个可靠的自动化机器健康状况监测系统可以实现有效的预测性维护实践，并直接有助于所有三个L。 这样的系统应该能够检测故障，将它们与根本原因联系起来，并预测未来的机器健康状态。 在机器状态监测的一般领域中，故障诊断的解决方案变得越来越复杂，故障分类的可靠性以及对操作和环境条件变化的鲁棒性得到提高。 然而，很少有解决方案可以确定故障的根本原因或预测系统级的故障。 这些是行业采用研究和开发成果缓慢的主要原因，因为仅仅检测故障而没有可操作的信息，例如何时何地发生故障，并不一定转化为成本节约。 包括基于底层物理原理的组件模型的系统级模型可以为具有急需的故障预测能力的解决方案提供关键。 在这项研究计划中，我们的目标是建立一个完整的风力涡轮机系统模型，可以代表一个数字拷贝的实际系统，特别是在其健康状况。 在任何时间点，该模型可以指示故障位置和严重性。它可以用来模拟系统的行为，作为未来故障发展的结果，以实现故障预测。 它还可以用于分析局部故障对系统整体动态行为和其他组件的健康的影响，以提供有效的干预。 研究成果的成功交付将代表着为风能和其他行业构建有效的预测性维护解决方案的飞跃。