Active Learning-Based Optimization of Hydroelectric Turbine Startup to Minimize Fatigue Damage

TL;DR

This paper presents an active learning-based method to optimize hydroelectric turbine startups, significantly reducing fatigue stress with minimal measurements, thereby extending turbine lifespan.

Contribution

It introduces a novel automated optimization approach combining active learning and simulations to minimize turbine stress during startups with limited measurement data.

Findings

Achieved 42% reduction in maximum strain cycle amplitude.

Successfully identified optimal startup sequences with only seven measurements.

Demonstrated effectiveness during real-time on-site testing.

Abstract

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.