Experimental Investigation of Repurposed Kaplan Turbines as Variable-Speed Propellers for Maximizing Frequency Containment Reserve
Francesco Gerini, Elena Vagnoni, Martin Seydoux, Rachid Cherkaoui and, Mario Paolone

TL;DR
This study demonstrates that repurposing aging Kaplan turbines as variable-speed propellers with frequency converters can enhance frequency containment reserve provision, improve hydraulic efficiency, and eliminate blade fatigue issues, offering a viable alternative to BESS hybridization.
Contribution
The paper presents a novel experimental validation of converting Kaplan turbines into variable-speed devices using full-scale frequency converters, comparing their performance to BESS hybrid systems.
Findings
Repurposed turbines show similar dynamic response to traditional operation.
Blade movements are effectively eliminated in the modified turbines.
Hydraulic efficiency increases at certain operating points.
Abstract
This study explores the practical viability of repurposing aging Kaplan turbines into variable-speed propellers by employing full-size frequency converters. The motivation behind this approach is to improve the provision of \emph{Frequency Containment Reserve} (FCR) while reducing fatigue in the Kaplan blades servomechanism. We evaluate the performance of these modified Kaplan turbines against the one of another hydro asset composed of the same Kaplan turbine hybridized with a \emph{Battery Energy Storage System} (BESS). Experiments are conducted on a one-of-its-kind reduced-scale model testing platform. Our findings reveal that Kaplan turbines repurposed as variable-speed propellers exhibit similar dynamic response characteristics compared to the standalone Kaplan operation, with the added benefit of effectively eliminating blade movements. Furthermore, the ability to control the speed…
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Taxonomy
TopicsCavitation Phenomena in Pumps · Advanced Combustion Engine Technologies · Turbomachinery Performance and Optimization
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
