Journal of Hydroelectric Engineering

水力发电学报

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2024 Vol. 43, No. 10 Published: 2024-10-25

	1	Study on capacity design for hybrid pumped storage-wind-photovoltaic multi-energy complementary system ^Hot!
		ZHANG Pengfei, MA Chao, LI Shiyu
		DOI: 10.11660/slfdxb.20241001
		The hybrid pumped storage-wind-photovoltaic multi-energy complementary system has broad application prospects. However, its capacity design needs to characterize the complex relationship between the water volume and electric power, and its economic evaluation should consider the rules of electricity markets. This paper describes a new two-stage optimization framework for optimizing operation and capacity decision. First, a consistent assumption for the target gross output is presented; and a double-objective operation optimization model is developed. Then, a discrete decision space is obtained through optimization based on a large number of medium and long-term operation cases. Finally, the scheme with the maximized net present value (NPV) is selected. Application in a case study of the clean energy base in the upper Yellow River gives the conclusion as follows. New energy capacities corresponding to high, medium and low acceptance degrees of load loss risks are 3.2-3.9 times, 2.4-3.0 times, and 1.6-2.1 times that of the mixed pumping and storage capacity, respectively. The peak to valley ratios of the system's monthly electricity delivery range from 1.36 to 1.45, indicating the power sources in the system are well complementary on the medium and long time scales.
		2024 Vol. 43 (10): 1-16 [Abstract] ( 73 ) PDF (6772 KB) ( 221 )

	17	State variable feedback-correction method of hydrological model based on ensemble Kalman filter
		WANG Wenpeng, HE Dianpeng, WU Yirui, QIU Peng, ZHANG Xinyue, LIU Bo
		DOI: 10.11660/slfdxb.20241002
		The ensemble Kalman filter approach has been used to correct the state variable in hydrological models. Difficulties of its application include how to select the state variable for correction, whether or not to synchronize parameter correction with the state variable, and how to set up the filter algorithm's hyperparameters. To address these issues, we take the calibrated GR5J model for the Qijiang River basin as a prototype tool to assimilate observed streamflows and correct model state variables using feedback correction. We use synthesis experiments and rolling forecast tests to examine the impacts of state variable selection, model parameter disruption, and hyperparameter optimization of the filter algorithm on forecast accuracy. The results suggest that while the biased initial state could be specified, the ensemble Kalman filter does raise forecast accuracy; otherwise, a better way is to fix the runoff generation variable and the flow confluence variable simultaneously to avoid overcorrection on model states. In the case of biased model parameters, it is best to identify the parameter first and then adjust the state variable. Increasing the ensemble members and warm-up periods generally improve correction accuracy, but the impacts of model noises and observation noises on the correction accuracy are non-monotonic. The filter algorithm is superior to the warm-up method, though its forecast accuracy decreases with an increasing forecast period. The findings would help apply the state correction method to operational forecasting.
		2024 Vol. 43 (10): 17-31 [Abstract] ( 65 ) PDF (5273 KB) ( 123 )

	32	Study on flood threshold of high-efficient sediment transport from Huayuankou to Sunkou in lower Yellow River
		BAI Yuchuan, LIANG Dong, LI Yan, HUANG Zhe, XU Haijue
		DOI: 10.11660/slfdxb.20241003
		Changes in the energy dissipation rate of a river reflect its ability to transport water and sediment and its patterns in bed morphology adjustment. This study explores the quantitative relationships of boundary resistance energy dissipation rate (BREDR) versus incoming water and sediment, cross-sectional morphology, and riverbed stability, from a perspective of energy dissipation, combined with statistical analysis of data measured in the lower Yellow reach of Huayuankou to Sunkou. We determine an efficient flood sediment transport threshold for this reach, based on two indicators: BREDR, and riverbed stability. The results show that the flow rate is the main factor of BREDR, and it is linearly related to the power of BREDR. As the flow increases above 2000 m3/s, its BREDR shows a trend of weakening first and then strengthening with the increase in sediment concentration. Generally, a river’s BREDR is negatively correlated with the width-to-depth ratio under high flow conditions. The correlation is low for the study reach of Huayuankou and Jiahetan where cross-sectional morphology is complex, while high at the cross sections of Gaocun and Sunkou with simple morphology. Under unsteady water and sediment conditions, the riverbed will mostly develop instability. Considering both bed morphological stability and the minimum of BREDR, our calculations reveal the flow rate threshold for efficient flood sediment transport in the study reach is roughly 1500 m3/s, with a sediment concentration of about 23 kg/m3.
		2024 Vol. 43 (10): 32-41 [Abstract] ( 42 ) PDF (2390 KB) ( 58 )

	42	Roughness inversion method for river unsteady flow simulations based on deep learning
		LI Chengye, PENG Yang, LUO Shiqi, YU Xianliang, YAO Lishuang
		DOI: 10.11660/slfdxb.20241004
		Manning’s roughness coefficient, as a comprehensive indicator of flow resistance, significantly affects the accuracy of one-dimensional unsteady flow simulations. Previous studies based on roughness inversion lack consideration of the roughness that varies with the discharge or water level. This paper develops a roughness inversion method for river unsteady flow simulations based on the long short-term memory neural network, through treating roughness as a continuous piecewise linear function of discharge, so as to realize the direct inversion of roughness using a data-driven method. We also develop a successive approximation based on a stepwise inversion strategy to reduce the dimension of inversion solutions, a useful technique for long natural rivers that feature a great number of cross sections and a large discharge variation range. This inversion method is evaluated through a case study of the reaches of the Xiangjiaba Reservoir, China. The results show that by using the roughness values inverted from the observed data under different discharge grades, its calculations of the water stage hydrographs are in good agreement with measurements, and its accuracy is significantly higher than the methods without considering roughness variations with discharge. The results verify the effectiveness of our new method that provides a novel approach to the roughness inversion of flows in long rivers. Keywords: long short-term memory neural network; stepwise inversion strategy; natural river; roughness inversion; one-dimensional unsteady flow
		2024 Vol. 43 (10): 42-52 [Abstract] ( 45 ) PDF (3697 KB) ( 97 )

	53	Numerical study on circulating flows and hydraulic losses in canned motor
		GUO Miao, LIU Yu, TANG Xuelin, LIU Shuhong, ZUO Zhigang, LI Xiaoqin
		DOI: 10.11660/slfdxb.20241005
		In this work, a RNG k-ε turbulence model is used to simulate the flows inside a canned motor, and flows in its stator section are calculated using a porous media model and the Ergun equation. We construct a theoretical model for hydraulic loss analysis based on energy conservation, and use it to reveal the time evolution of the motor’s hydraulic loss. Flow patterns are analyzed in details. The results show that spiral flow patterns occur in the gap between the rotor and porous medium, and the impacting water flow forms multiple axisymmetric vortices in this gap, which are attributed to the larger resistance in the porous medium. Near the thrust bearing, some axisymmetric vortices similar to the blade flow are located at its radial flow channel inlet, and high-vorticity circulating flows develop in the flow outside of it. These findings suggest a complex geometric structure may result in more vortices and cause a significant hydraulic loss, especially between the thrust bearings and sliding bearings. This study would be a useful reference for optimizing the geometric parameters and structure of related motors.
		2024 Vol. 43 (10): 53-62 [Abstract] ( 41 ) PDF (1793 KB) ( 101 )

	63	Numerical study on dynamic behaviors of cavitation bubbles near through-hole flat plate
		MA Mingkai, YIN Jianyong, GONG Dehong, ZHANG Yongxue, DU Xianrong
		DOI: 10.11660/slfdxb.20241006
		The effectiveness of cavitation cleaning process is closely linked to the ratio of the distance between a cavitation bubble and the orifice plate to its maximum radius in this process. However, most of the previous studies on cavitation bubble dynamics in the vicinity of a through-hole flat plate were limited to the factors such as morphology and jet direction, with little attention given to jet strength or evolutionary period. In this work, a two-phase compressible cavitation bubble dynamics model is used to study the multi-period evolution dynamics of cavitation bubbles near a flat plate with a through-hole, based on the open source fluid dynamics code OpenFOAM. Simulation results reveal that the maximum velocity of the liquid jet, formed by cavitation bubble collapsing, decreases as the distance parameter γ increases when it reaches the center of the hole. As the size parameter ε decreases, this liquid jet's maximum velocity at the hole's center increases. And, with a decrease in γ or ε, the minimum radius of a cavitation bubble at its first collapse increases gradually, the intensity of bubble collapsing slows down, and the time period of the first collapse is prolonged.
		2024 Vol. 43 (10): 63-75 [Abstract] ( 37 ) PDF (4684 KB) ( 101 )

	76	Response characteristics of large fixed-speed and variable-speed pumped-storage units under fast power fluctuations
		WANG Yuqing, ZHOU Daqing, FENG Chen
		DOI: 10.11660/slfdxb.20241007
		This study compares the responses of large fixed-speed and variable-speed pumped-storage units to fast power fluctuations through numerical simulations. We examine their response characteristics under power fluctuations of nine sets different in amplitude and frequency. The power response characteristics of the units are analyzed using four quantitative evaluation indicators－mean and standard deviations of power differences, mean response time for power rise (fall), and power delay. The results show that variable-speed generating units not only effectively respond to rapid fluctuations in the renewable energy output within seconds, but their response capabilities are superior to those of traditional large fixed-speed pumped-storage units. This further emphasizes the importance of variable speed units in coping with renewable energy fluctuations.
		2024 Vol. 43 (10): 76-84 [Abstract] ( 35 ) PDF (2498 KB) ( 69 )

	85	Research on simulation method of high arch dam construction process under cold conditions
		GUAN Tao, XIAO Yifeng, REN Bingyu, YU Hao
		DOI: 10.11660/slfdxb.20241008
		Construction of many high arch dams is faced with harsh cold weather conditions. However, few previous methods for the simulation directly consider the impact of temperature on construction or the dynamic warehousing based on temperature predictions for utilizing fully the unfreezing temperature period for concrete pouring in the cold season. This paper presents a new method for simulating the construction process of high arch dams under cold conditions based on Informer. First, we establish a temperature prediction model based on Informer to predict future temperature sequences, and apply it to construction analysis. Then, we develop a dynamic partition construction model for constructing a high arch dam under cold conditions, and implement a partition construction simulation strategy applicable in real construction environment, so as to improve the construction simulation and analysis for different temperature conditions. Finally, the Informer model is used to predict temperature in a case study of the Yebatan high arch dam project located in the southwest region, generating an average error of ±1.49 ℃ and a daily average error of ±1.16 hours of construction time. We compare three different simulation strategies and verify that the dynamic partition strategy is more efficient under cold conditions, demonstrating temperature factor consideration gives improved simulations that are closer to reality than the method of simply reducing efficiency.
		2024 Vol. 43 (10): 85-96 [Abstract] ( 33 ) PDF (2676 KB) ( 103 )

	97	MTSVR-ISAO based inversion method for concrete gravity dam parameters
		CAO Wenhan, MA Lin, SU Huaizhi
		DOI: 10.11660/slfdxb.20241009
		Parameter identification of a concrete dam is the key to evaluating its behaviors. To further improve the efficiency and accuracy of parameter inversion, this paper develops a novel inversion strategy for concrete dam parameters based on the multi-output twin support vector regression (MTSVR) and the improved snow ablation optimization (ISAO). A nonlinear relationship of the parameters to be inverted versus the hydrostatic pressure component of displacement is simulated by training the MTSVR model, in place of complex finite element calculations; ISAO is used for optimizing the inversion of the target parameters. Analysis of engineering examples shows that the results of this surrogate model basically agree with those of the finite element calculations, ISAO has faster convergence and higher accuracy than the traditional meta-inspired optimization, and the computational cost of single parameter inversion is lower. This verifies that our new inversion strategy effectively improves computational efficiency while maintaining the computational accuracy, and that the method is effective, practical and useful for the parameter identification of actual projects.
		2024 Vol. 43 (10): 97-106 [Abstract] ( 30 ) PDF (2512 KB) ( 52 )

	107	Explanatory intelligent prediction model for deformation mechanism of super-high arch dam
		MA Chunhui, YU Fei, CHENG Lin, YANG Jie
		DOI: 10.11660/slfdxb.20241010
		Aimed at the limitation of the traditional intelligent black box model that cannot explain the deformation mechanism of arch dams, we apply the Shapley Additive Explanation (SHAP) theory and deconstruct a machine learning deformation prediction model for a super high arch dam, focusing on analysis of the influence of water pressure, temperature and aging on the radial horizontal displacement of its different parts. From the deformation monitoring data of the dam, we construct a Light Gradient Boosting Machine (LightGBM) black box prediction model that uses SHAP to eliminate the factors with multicollinearity, and analyze the contribution of different influencing factors to model deformation prediction for the whole factor set and a single sample. In a case study of the dam abutment of a super high arch, we examine the relationship of the influencing factors versus its radial horizontal displacements at dam foundation, arch crown, and other dam parts. We find the aging factor has a greater influence on the displacements at the higher elevations close to the arch crown. The temperature factor mainly affects the displacements near the arch crown, and the water pressure factor mainly affects those at higher elevations; while neither of both factors has a considerable effect on the displacements at the measuring points in the dam foundation and the rock mass deep into the abutment. Our method overcomes the shortcoming of poor visibility and unclear internal mechanism of the previous intelligent 'black box' deformation prediction model. Thus, this interpretable model yields the relevant laws that help working performance analysis and operation management of super high arch dams.
		2024 Vol. 43 (10): 107-120 [Abstract] ( 36 ) PDF (4701 KB) ( 92 )

	121	Dam deformation interval prediction model based on XGBoost
		CHEN Xianhao, HU Yu, WANG Yajun, ZHU Xuezhou
		DOI: 10.11660/slfdxb.20241011
		During the operation of a dam, its original monitoring data exhibit complex, diverse, and time-varying characteristics, leading to gradual reduction in the effectiveness and accuracy of long-term monitoring warnings and thereby increasing disaster risks. Therefore, developing efficient and accurate deformation monitoring models is crucial to dam safety assessment. Traditional deterministic point predictions of a dam system, due to its inherent uncertainty, are faced with unavoidable challenges in error, bringing in low accuracy and a difficulty in determining the main factors of dam deformation. This paper presents a novel method that combines eXtreme Gradient Boosting with Bootstrap to construct prediction intervals. We use Elastic Net to extract the features of displacement influencing factors, and Bayesian Optimization to search for its optimal parameters. It can effectively estimate its own bias by combining multiple XGBoost models through Bootstrap; through residual training of the ensemble model, it further estimates the variance of random noise, quantifying the uncertainty of dam deformation. We validate this method in engineering case studies against the monitoring data from the Baihetan extra high arch dam under operation. Comparison of its predictions with the measurements and those predicted using a single model verifies its high accuracy and robustness, showing its root mean square error of only 0.0112. The accuracy of the model reaches 96%, and the efficiency is raised by up to 71% compared with the single model.
		2024 Vol. 43 (10): 121-136 [Abstract] ( 44 ) PDF (2019 KB) ( 62 )