Damping characteristics analysis of hydropower units under full operating conditions and control parameters: Accurate quantitative evaluation based on refined models

Liu, Dong; Li, Chaoshun; Tan, Xiaoqiang; Lu, Xueding; Malik, O.P.

doi:10.1016/j.apenergy.2021.116881

Cited by 29 publications

(8 citation statements)

References 44 publications

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“…In nonlinear fitting, back propagation neural network (BPNN), which has strong nonlinear mapping ability, is widely used in modeling. [12][13][14] In terms of intelligent optimization algorithms, slime mold algorithm (SMA) simulates the behavior strategies of slime molds when searching for food. [15][16][17] It has the characteristics of a simple structure, fast optimization speed, and can achieve a balance between local optimization and global search.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of geometric parameters of hydraulic turbine runner in turbine mode based on ISMA and BPNN

Wang,

Chen,

Zhang

et al. 2023

Energy Science & Engineering

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The hydraulic turbine in turbine mode (TMHT) has significant advantages in residual energy recovery, but rapid optimization of its runner parameters has always been a challenge. To address this issue, the optimization algorithm ISMA‐BPNN is proposed based on the improved slime mold algorithm (ISMA) and back propagation neural network (BPNN). First, an efficiency characteristic neural network (ECNN) and a water head characteristic neural network (HCNN), which take the geometric parameters of the runner as input, and the efficiency and water head as outputs, respectively, are constructed by combining the orthogonal test‐based sample data, computational fluid dynamics (CFD), and BPNN. Then, the slime mold algorithm is improved and the optimal runner geometric parameters are obtained based on the ISMA, ECNN, and HCNN, to achieve rapid optimization of the TMHT runner. Finally, the CFD‐based numerical calculation accuracy is verified through real machine tests, and the feasibility of the ISMA‐BPNN‐based rapid optimization strategy for TMHT performance is further verified through CFD numerical simulation.

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Section: Introductionmentioning

confidence: 99%

“…In nonlinear fitting, back propagation neural network (BPNN), which has strong nonlinear mapping ability, is widely used in modeling 12–14 . In terms of intelligent optimization algorithms, slime mold algorithm (SMA) simulates the behavior strategies of slime molds when searching for food 15–17 .…”

Section: Introductionmentioning

confidence: 99%

Optimization of geometric parameters of hydraulic turbine runner in turbine mode based on ISMA and BPNN

Wang,

Chen,

Zhang

et al. 2023

Energy Science & Engineering

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“…2,3 Also, employing mathematical modelling analysis, stability and damping is studied for large hydropower units. 4,5 Furthermore, hydropower can be categorised into many types where one of them is micro-hydropower plants. According to Timilsina et al, 6 micro-hydropower plants are small scale systems that can produce up to 100 kW.…”

Section: Introductionmentioning

confidence: 99%

“…Through a hydro‐turbine nonlinearity approach, a stability analysis has been performed where results are helpful for practical applications 2,3 . Also, employing mathematical modelling analysis, stability and damping is studied for large hydropower units 4,5 …”

Section: Introductionmentioning

confidence: 99%

Performance analysis of a runner for gravitational water vortex power plant

Faraji

Jande

Kivevele

2022

Energy Science & Engineering

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“…Ensuring global energy access to all constitutes one of United Nations' sustainable development goals [1]. At the same time, there is a global will to shift the energy generation dependence from fossil fuels to renewable energy sources such as solar, wind, and hydropower [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Experimental Francis Turbine Cavitation Performances of a Hydro-Energy Plant

Zhao

Binama

et al. 2022

Sustainability

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An investigation is conducted on the Francis turbine’s cavitation characteristics and its influence on system hydraulic stability using two experimental methods, namely the flow visualization and acoustic emission methods. The investigated turbine is of Francis type with a 15-blade runner and has a specific speed of 202 rpm and a rated head of 30 m. Having tested the machine under a wide range of cavitation conditions, the gap cavitation is the earliest to develop as the cavitation coefficient gradually decreases and has no obvious effect on the machine’s external performance characteristics. The airfoil cavitation follows and causes the increase and decrease in machine flow rate and head, respectively, showing its drag reduction effect, where, at the same time, the pressure pulsation amplitude gets to its peak value. There is also the formation of constant cavitation zones and the involvement of an unsteady surge close to the wall of the draft tube’s cone. Pushing the cavitation coefficient to even lower values, there is the formation of an annular cavitation zone, accompanied by a sharp drop in cone pressure pulsation amplitudes while the former drag reduction effect disappears. The trend of noise is basically the same as that of pressure fluctuation, which confirms its trustworthiness when it comes to cavitation occurrence detection within Francis turbines.

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Damping characteristics analysis of hydropower units under full operating conditions and control parameters: Accurate quantitative evaluation based on refined models

Cited by 29 publications

References 44 publications

Optimization of geometric parameters of hydraulic turbine runner in turbine mode based on ISMA and BPNN

Optimization of geometric parameters of hydraulic turbine runner in turbine mode based on ISMA and BPNN

Performance analysis of a runner for gravitational water vortex power plant

Experimental Francis Turbine Cavitation Performances of a Hydro-Energy Plant

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