Synchronous condenser operation in Francis turbines: Effects in the runner stress and machine vibration

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

doi:10.1016/j.renene.2019.07.041

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…If these operational metrics exceed specific thresholds, alarms are activated. The emphasis on vibration and oscillation signals as key detection indices stems from two primary reasons: first, these signals are exceptionally convenient to monitor, and second, the majority of hydraulic turbine malfunctions manifest externally as excessive vibration or oscillation [ 13 , 14 , 15 , 16 ]. Consequently, accurately forecasting the trend changes in hydraulic turbine vibration and oscillation has emerged as a novel research focus [ 17 , 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

A Multistep Prediction Model for the Vibration Trends of Hydroelectric Generator Units Based on Variational Mode Decomposition and Stochastic Configuration Networks

Yan,

Chen,

Yang

et al. 2023

Sensors

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Accurately predicting the changes in turbine vibration trends is a key part of the operational condition maintenance of hydropower units, which is of great significance for improving both the operational condition and operational efficiency of hydropower plants. In this paper, we propose a multistep prediction model for the vibration trend of a hydropower unit. This model is based on the theoretical principles of signal processing and machine learning, incorporating variational mode decomposition (VMD), stochastic configuration networks (SCNs), and the recursive strategy. Firstly, in view of the severe fluctuations of the vibration signal of the unit, this paper decomposes the unit vibration data into intrinsic mode function (IMF) components of different frequencies by VMD, which effectively alleviates the instability of the vibration trend. Secondly, an SCN model is used to predict different IMF components. Then, the predicted values of all the IMF components are superimposed to form the prediction results. Finally, according to the recursive strategy, a multistep prediction model of the HGU’s vibration trends is constructed by adding new input variables to the prediction results. This model is applied to the prediction of vibration data from different components of a unit, and the experimental results show that the proposed multistep prediction model can accurately predict the vibration trend of the unit. The proposed multistep prediction model of the vibration trends of hydropower units is of great significance in guiding power plants to adjust their control strategies to reach optimal operating efficiency.

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Section: Introductionmentioning

confidence: 99%

A Multistep Prediction Model for the Vibration Trends of Hydroelectric Generator Units Based on Variational Mode Decomposition and Stochastic Configuration Networks

Yan,

Chen,

Yang

et al. 2023

Sensors

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Dynamic behaviour of a Francis turbine during voltage regulation in the electrical power system

Vagnoni

Valentín

Avellan

2021

International Journal of Electrical Power & Energy Systems

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Selection and Optimization of Sensors for Monitoring of Francis Turbines

Valentín

Presas

Valero

et al. 2021

IOP Conf. Ser.: Earth Environ. Sci.

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With the current demand of flexibility to hydraulic turbines, these are working under off-design conditions more than ever. Under these conditions different complex hydraulic phenomena appear, especially in Francis turbines, affecting the machine stability as well as reducing the useful life of its components. Hence, it is desirable to detect in real-time these hydraulic phenomena to assess the operation of the machine. In addition, the position and type of sensor to detect these phenomena is a key point for the monitoring of Francis units. In this paper, a large medium-head Francis turbine was instrumented with several sensors such as accelerometers, proximity probes, strain gauges, pressure sensors and a microphone. Results obtained with the different sensors in different locations are compared to know which hydraulic phenomenon is detected with every sensor. Furthermore, different signal analysis techniques are also compared. With this information, monitoring systems can be optimized with the most convenient sensors, locations and signal analysis techniques, reducing its difficulty to install and economic cost.

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Synchronous condenser operation in Francis turbines: Effects in the runner stress and machine vibration

Cited by 3 publications

References 19 publications

A Multistep Prediction Model for the Vibration Trends of Hydroelectric Generator Units Based on Variational Mode Decomposition and Stochastic Configuration Networks

A Multistep Prediction Model for the Vibration Trends of Hydroelectric Generator Units Based on Variational Mode Decomposition and Stochastic Configuration Networks

Dynamic behaviour of a Francis turbine during voltage regulation in the electrical power system

Selection and Optimization of Sensors for Monitoring of Francis Turbines

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