Digital Twins of hydroelectric generating units: AI insight from sensor data combined with physics-based simulation

水力发电机组的数字孪生：传感器数据的人工智能洞察与基于物理的模拟相结合

• 批准号: 556353-2020
• 负责人: Gosselin, FrederickFP
• 金额: $ 9.5万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=754301
• 关键词: Digital; Twins; hydroelectric; generating; units

The operation of hydroelectric generating units is evolving, creating the need for better condition monitoring and accurate modeling of the power-generating equipment. Whereas hydro units used to be overdesigned and continuously operated at baseload conditions, today's new units and refurbished ones are designed with stringent performance and operational regime requirements. Notably, they are relied upon for power regulation to integrate more intermittent wind and solar energy in the grid and must undergo frequent starts and stops. The consequence of all this is greater stresses on the units, which can lead to failures and loss of productivity. It is thus essential to better monitor the equipment and predict its behaviour and outcome.Polytechnique Montreal, Hydro Quebec and Maya HTT will work together to develop the tools to build the Digital Twin of a hydro unit. Such a digital twin will combine live sensor data with physics-based modeling through artificial intelligence to achieve real-time simulation of a hydro unit. It will allow predicting failures, optimising maintenance schedules, and simulate scenarios of usage and wear of the equipment.To this end, a group of more than 10 highly qualified personnel will use Physics-Informed Neural Networks and Proper Generalized Decomposition to develop reduced-order models of academic model systems exhibiting some of the same physics as Hydro Quebec's equipment and incorporate them in Maya HTT's software solution. This gradual approach will enable us to overcome the same challenges required to combine experimental measurements and physical modeling with artificial intelligence to develop digital twins for real industrial equipment.

水力发电机组的运行是不断发展的，需要更好的状态监测和发电设备的准确建模。过去，水力发电机组被过度设计，并在基本负荷条件下持续运行，而如今，新建和翻新的水力发电机组的设计具有严格的性能和运行制度要求。值得注意的是，它们被用于电力调节，以将更多间歇性的风能和太阳能纳入电网，并且必须频繁启动和停止。所有这一切的后果是对单位的更大压力，这可能导致失败和生产力的损失。因此，必须更好地监测设备并预测其行为和结果。蒙特利尔理工大学、魁北克水电公司和玛雅HTT公司将共同开发工具，以建立水力发电装置的数字双胞胎。这种数字孪生将通过人工智能将实时传感器数据与基于物理的建模相结合，实现水力发电机组的实时仿真。它可以预测故障，优化维护计划，并模拟设备的使用和磨损情况。为此，一个由10多名高素质人员组成的小组将使用物理信息神经网络和适当的广义分解来开发学术模型系统的降阶模型，展示与Hydro Quebec设备相同的一些物理特性，并将其纳入Maya HTT的软件解决方案。这种渐进的方法将使我们能够克服将实验测量和物理建模与人工智能相结合以开发真实工业设备的数字孪生所需的相同挑战。