Detection of Hydraulic Phenomena in Francis Turbines with Different Sensors

Valentín, David; Presas, Alexandre; Valero, Carme; Egusquiza, Eduard

doi:10.3390/s19184053

Cited by 19 publications

(11 citation statements)

References 31 publications

(43 reference statements)

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“…Since then, broad research on these tools has been conducted; many papers have been published on the vibroacoustical assessment of cavitation in hydro turbines, reversible pump turbines, pumps, and on special experimental gadgets. This research has incorporated the optimisation of the locations of vibro-acoustical sensors on the machinery , the optimisation of sensor types [8][9][10]13,[15][16][17][18][19]23,[25][26][27][28] the development of suitable data processing methods, and clarification of the relation between vibro-acoustical quantities and cavitation intensity . The crucial question here has been whether one can vibro-acoustically distinguish between erosive and non-erosive cavitation and quantify the cavitation erosivity.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Vibro-Acoustical Diagnostics of Cavitation: Theory and Illustration on a Kaplan Turbine

Bajic¹,

Weissenberger

Keller

2022

Fluids

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Korto’s multidimensional method for vibro-acoustical diagnostics and monitoring of turbine cavitation is based on a high number of spatially distributed sensors and the signal and data processing that systematically utilises three data dimensions: spatial, temporal, and operational. The method delivers unbiased data on cavitation intensity and rich diagnostical data on cavitation mechanisms. It is applicable on Kaplan, Francis, bulb, and reversible pump turbines, as well as pumps. In this paper, the theory of the method is introduced, and its application is illustrated on a prototype and three models of a Kaplan turbine. In the considered case, two distinct cavitation mechanisms responsible for the two erosion patches found in an overhaul are vibro-acoustically identified, quantified, and analysed. The cavitation quality of the models is compared. Cavitation as a source of vibration is discussed.

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Section: Introductionmentioning

confidence: 99%

Multidimensional Vibro-Acoustical Diagnostics of Cavitation: Theory and Illustration on a Kaplan Turbine

Bajic¹,

Weissenberger

Keller

2022

Fluids

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“…Due to this, turbulence and high-level pressure pulsations due cavitation, load variation and rotor-stator interaction can lead to undesirable excitations in the runner. These excitations can generate high vibration amplitudes in the runner, which can induce a useful life shortening or a premature failure in the worst cases [4][5][6][7][8][9][10]. In order to avoid the critical operating conditions, it is of paramount importance to analyze the dynamic response of the runners.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Mode Shapes of Kaplan Runners

et al. 2022

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To prevent lifetime shortening and premature failure in turbine runners, it is of paramount importance to analyse and understand its dynamic response and determine the factors that affect it. In this paper, the dynamic response of a Kaplan runner is analysed in air by numerical and experimental methods. First, to start the analysis of Kaplan runner mode shapes, its geometry is simplified and modelled as a bladed disk. Bladed disks with different blade numbers are investigated, by numerical simulation, in order to understand the influence of this parameter on its modal characteristics. Then, mode shapes extracted are characterized and a classification is proposed. Second, an existing Kaplan runner is simulated by Finite Elements Method (FEM) and its mode shapes are extracted. The obtained results are contrasted with the bladed disks mode shapes, in order to validate the classification proposed. The simulated Kaplan runner is also experimentally studied. A numerical modal analysis is carried out in the real runner. Different, global and local, mode shapes are identified. The global mode shapes extracted by numerical and experimental modal analysis are compared and discussed. Finally, the local mode shapes identified are commented and explained by means of numerical simulation.

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“…Other types of sensors like acoustic emission sensors, strain gauges, and microphones have also been tested to monitor the condition of hydraulic turbines. [8][9][10] One of the most complicated tasks is to select alarm and trip levels which are able to detect incipient damage in the runner. 6 To begin with, the levels of each indicator have to be mapped for the whole operating range when the machine is in good condition.…”

Section: Introductionmentioning

confidence: 99%

Improved damage detection in Pelton turbines using optimized condition indicators and data-driven techniques

Zhao

Egusquiza

Estévez

et al. 2021

Structural Health Monitoring

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The health condition of hydraulic turbines is one of the most critical factors for the operation safety and financial benefits of a hydro power plant. After the massive entrance of intermittent renewable energies, hydropower units have to regulate their output much more frequently for the balancing of the power grid. Under these conditions, the components of the machine have to withstand harsher excitation forces, which are more likely to produce damage and eventual failure in the turbines. To ensure the reliability of these machines, improved condition monitoring techniques are increasingly demanded. In this article, the feasibility of upgrading condition monitoring of Pelton turbines using novel vibration indicators and data-driven techniques is discussed. The new indicators are selected after performing a detailed analysis of the dynamic behavior of the turbine using numerical models and field measurements. After that, factor analysis is carried out in order to assess which are the most informative indicators and to reduce the dimension of the input data. For the validation of the proposed method, monitoring data from an actual Pelton turbine that suffered from an important fatigue failure due to a crack propagation on the buckets have been used. The novel condition indicators as well as classical indicators based on the spectrum and harmonics levels have been obtained while the machine was in good operation, during different stages of damage and after repair. All of these have been used to train an artificial neural network model in order to predict the evolution of the crack until failure occurs. The results show that using the improved monitoring methodology enhances the ability to predict the appearance of damage in comparison to typical condition indicators.

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Detection of Hydraulic Phenomena in Francis Turbines with Different Sensors

Cited by 19 publications

References 31 publications

Multidimensional Vibro-Acoustical Diagnostics of Cavitation: Theory and Illustration on a Kaplan Turbine

Multidimensional Vibro-Acoustical Diagnostics of Cavitation: Theory and Illustration on a Kaplan Turbine

Analysis of the Mode Shapes of Kaplan Runners

Improved damage detection in Pelton turbines using optimized condition indicators and data-driven techniques

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