Dynamic response of Pelton runners: Numerical and experimental analysis in prototypes

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

doi:10.1016/j.renene.2020.04.110

Cited by 15 publications

(10 citation statements)

References 16 publications

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“…In order to implement the new condition indicators on the monitoring system, an extensive analysis of the dynamic behavior of the turbine was carried out. 31,38 First, the natural frequencies of the machine were determined by means of numerical and experimental analysis on the prototype. After that, the response (i.e.…”

Section: New Condition Monitoring Methodsmentioning

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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Section: New Condition Monitoring Methodsmentioning

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

Self Cite

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“…In [15], a detailed analysis of the modal characteristics of four Pelton turbine prototypes is presented, with a focus on the identification of dominant modes of vibration of the runners dynamic response. The comparison between the performed experimental and numerical study indicates that the bucket-dominated modes are predominant in terms of vibration amplitude compared to the disk-dominated ones, especially for circumferential vibrations.…”

Section: Number Of Buckets (-)mentioning

confidence: 99%

“…Given the preminence of bucket-dominated modes compared to the disk-dominated ones in terms of vibration amplitude [15], the disk-bucket system shown in Fig. 1 is modeled as a cantilever beam supported by an infinitely rigid wall as in Fig.…”

Section: The Bucketmentioning

confidence: 99%

“…The variation of the injector opening rate value 𝑦 𝑖𝑛𝑗 with time is determined by both the physical reactivity of the needle's movement and the safety limits imposed by the HPP operators to avoid detrimental consequences induced by hydraulic transient phenomena. This constraint is formulated as follows: (15) with 𝑘 𝑦 a constant value.…”

Section: Mechanical System Constraintsmentioning

confidence: 99%

See 1 more Smart Citation

Structural Impact of the Start-Up Sequence on Pelton Turbines Lifetime: Analytical Prediction and Polynomial Optimization

Alerci¹,

Vagnoni²,

Paolone³

2023

Preprint

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“…Once a fault induces in any component, it may cause damage to other components and may result in a complete shutdown of the plant. In the Pelton wheel, the servomotor controls the sharp needle in the nozzle to regulate the discharge [3][4][5][6]. The water coming out of the injector hits the buckets to apply the torque on the rotor.…”

Section: Introductionmentioning

confidence: 99%

Adaptive MOMEDA model based variational mode decomposition for Pelton wheel fault detection

Vashishtha¹,

Chauhan²,

Yadav³

et al. 2022

Eng. Res. Express

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A critical step in fault diagnosis is determining the frequency of faults. Variational mode decomposition (VMD) is extensively employed for this purpose since it can describe the signal in the time-frequency domain. On the other hand, the VMD frequently fails to analyse non-stationary data containing low-frequency disturbances/noises. A multipoint optimal minimal entropy deconvolution adjusted (MOMEDA) is used with VMD in this research to improve defect detection performance in the presence of low-frequency disturbances. The filter length has a strong influence on the output of MOMEDA thus choosing the right one is a critical step in recovering a periodic pulse in the event of a weak defective signal. Improved grey wolf optimization (GWO) adaptively selects the appropriate filter length using the autocorrelation energy as its fitness function. The GWO is improved by introducing a gaussian mutation strategy which maintains the proper balance between the exploration and exploitation process. The proposed method has been applied to investigate the bucket defects of the Pelton wheel. The raw vibration signal is first decomposed into a series of modes using VMD. Second, the MOMEDA model is used to purify each mode by reducing low-frequency noise interference. The modes processed by MOMEDA are reconstructed again into a pure signal. Finally, the Hilbert envelop spectrum of the pure signal is obtained to determine the fault frequency. The same is verified from the theoretical fault feature frequency of the turbine bucket. The performance of the proposed method in extracting fault frequency accurately is also compared with other two models: a) MOMEDA model with ensemble empirical mode decomposition (EEMD) and b) MOMEDA model with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The comparison results proved the efficacy and superiority of the proposed method.

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Dynamic response of Pelton runners: Numerical and experimental analysis in prototypes

Cited by 15 publications

References 16 publications

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

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

Structural Impact of the Start-Up Sequence on Pelton Turbines Lifetime: Analytical Prediction and Polynomial Optimization

Adaptive MOMEDA model based variational mode decomposition for Pelton wheel fault detection

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