用少次实测优化水轮机启动，减损42%疲劳损伤。
Active Learning-Based Optimization of Hydroelectric Turbine Startup to Minimize Fatigue Damage
- 结合主动学习与黑箱优化，仅需7次实测即找到最优启动参数。
- 实测显示最大应变循环幅度降低42%，显著减轻机械损耗。
- 适合关注水电设备寿命、运维优化的工程团队参考。
水力发电机组（HGUs）因灵活调节能力，在整合间歇性可再生能源中发挥关键作用。然而，频繁启停带来的瞬态过程加剧了涡轮机应力，导致疲劳累积和寿命缩短。为解决此问题，本文提出一种基于主动学习与黑箱优化的自动化方法，在有限实测次数下优化启动参数。该方法利用虚拟应变传感器与动态仿真，结合现场实测数据进行验证。在一台装有传感器的弗朗西斯式水轮机原型上测试表明，仅使用7次实测序列，算法即成功识别出最优启动方案，使最大应变循环幅度较标准启动序列降低42%。该研究为高效优化水电机组启停提供了可行路径，有望延长其运行寿命。
原文摘要 · Abstract (English)
Hydro-generating units (HGUs) play a crucial role in integrating intermittent renewable energy sources into the power grid due to their flexible operational capabilities. This evolving role has led to an increase in transient events, such as startups, which impose significant stresses on turbines, leading to increased turbine fatigue and a reduced operational lifespan. Consequently, optimizing startup sequences to minimize stresses is vital for hydropower utilities. However, this task is challenging, as stress measurements on prototypes can be expensive and time-consuming. To tackle this challenge, we propose an innovative automated approach to optimize the startup parameters of HGUs with a limited budget of measured startup sequences. Our method combines active learning and black-box optimization techniques, utilizing virtual strain sensors and dynamic simulations of HGUs. This approach was tested in real-time during an on-site measurement campaign on an instrumented Francis turbine prototype. The results demonstrate that our algorithm successfully identified an optimal startup sequence using only seven measured sequences. It achieves a remarkable 42% reduction in the maximum strain cycle amplitude compared to the standard startup sequence. This study paves the way for more efficient HGU startup optimization, potentially extending their operational lifespans.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。