Journal of Hydroelectric Engineering

水力发电学报

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2023 Vol. 42, No. 11 Published: 2023-11-25

	1	Pressure treatment and characteristic analysis of load rejection tests for pumped storage power station
		LIN Wenwen, YU Xiaodong, CHEN Xiaojiang, LIU Guoping, ZHOU Tingxin
		DOI: 10.11660/slfdxb.20231101
		For load rejection in a pumped storage power station, noise interference usually makes it difficult to extract pressure pulsation information accurately from pressure signals at its volute inlet. This paper presents a joint reduction method that combines the variational mode decomposition (VMD) and the complete ensemble empirical mode decomposition of adaptive noise (CEEMDAN). This method first decomposes a pressure signal using VMD and reconstructs its components based on mutual information (MI) to reduce the permutation entropy (PE). Then, it decomposes the reconstructed signal using CEEMDAN and superimposes the components, so as to obtain the modified pressure data sequence that features a permutation entropy close to that of the simulation signal. Engineering case studies show that our new processing method is quite accurate in decomposing pressure signals measured at the volute inlet, and its use of mutual information improves the accuracy of component reconstruction using correlation coefficients. The results would promote accurate extraction and analysis of pressure pulsation in future.
		2023 Vol. 42 (11): 1-10 [Abstract] ( 96 ) PDF (1964 KB) ( 358 )

	11	Instability mechanism of pump power-trip of ultra-high head pump turbines
		LYU Jingwei, FU Xiaolong, LI Deyou, WANG Hongjie, WEI Xianzhu
		DOI: 10.11660/slfdxb.20231102
		The ultra-high head pumped storage unit connects to a long water pipeline system. In its transient process, pressure pulsation and flow evolution are more complicated, and the hydraulic instability often occurs in the unit. Based on the one-and-three-dimensional model-coupling method and the dynamic mesh technology, this paper conducted numerical simulation on the pump power-trip process of an ultra-high head pumped storage unit. We examine the transient characteristics and high amplitude pressure pulsation by combining time-frequency analysis and internal flow field analysis, focusing on the instability development mechanism. Apart from the high frequency pressure pulsation caused by the rotor-stator interference during the pump power-trip process, this study shows that in the stay vane area, the low-frequency pressure pulsations occurred are induced by the rapid movement of the vortices and local backflows at the high-pressure inlet of the runner. While in the guide vane area, the high amplitude pulsations are caused by the flow collision. The results are meaningful for the hydraulic design and operation of ultra-high head pumped storage units.
		2023 Vol. 42 (11): 11-20 [Abstract] ( 77 ) PDF (4923 KB) ( 287 )

	21	Study on long-term stochastic optimal operation of cascade reservoirs by deep reinforcement learning
		LI Wenwu, ZHOU Jiani, PEI Benlin, ZHANG Yifan
		DOI: 10.11660/slfdxb.20231103
		Compared with a single reservoir, cascade reservoirs operation features a state space increasing exponentially. This paper describes a Deep Q-network (DQN) algorithm for deep reinforcement learning to solve the dimension disaster problem that is faced by the table-based reinforcement learning method in optimizing the long-term operation of cascade reservoirs. First, we derive a joint distribution function of stochastic inflow runoffs of the reservoirs based on the Copula function. Then, following the idea of time series difference, we construct a target neural network and a main neural network for approximating the values of the current action state and the next action state, respectively, and use ε-greedy algorithm to obtain optimal operation policy. Finally, the main parameters of reservoir operation are optimized by step to ensure operation efficiency. Compared with the Q-learning algorithm or its modification, the DQN algorithm improves the objective value of optimal scheduling, accelerates convergence, and avoids dimension disaster effectively in the optimization of cascade reservoirs operation.
		2023 Vol. 42 (11): 21-32 [Abstract] ( 147 ) PDF (1827 KB) ( 293 )

	33	Analysis of extreme precipitation characteristics under future low-emission scenarios. Case study of Jialing River basin
		MENG Changqing, LIU Keying, DONG Zijiao, WANG Yuankun, WU Qiyue, ZHONG Deyu, MEN Baohui
		DOI: 10.11660/slfdxb.20231104
		Disastrous consequences following an extreme event of precipitation are related not only to its total volume and peak value, but also to its time distribution pattern. Based on the daily precipitation data of in-situ measurements and global climate model projections, this paper improves the three-segment deviation correction method, and examines the extreme precipitation events (daily extreme precipitation and the preceding and succeeding precipitation) in the Jialing River basin using a time distribution model, focusing on their historical distribution characteristics and the future trends in the frequency, volume, duration, and concentrating patterns of precipitation. The results show that the new improved method generates a more significant correction effect than that of the three-segment method, and a more effective reduction of the simulation errors against measurements. Compared with the historical period, the volumes of precipitation of two major types show an increasing trend and their durations are prolonged, while their concentration ratios take a decreasing trend. In the future projection period, the frequency, volume, duration, and concentration of extreme precipitation events all present an increasing trend followed by a decreasing trend.
		2023 Vol. 42 (11): 33-45 [Abstract] ( 77 ) PDF (8642 KB) ( 235 )

	46	Evolution and synchronization characteristics of flood elements in Loess Plateau watershed. Case study of Kuye River
		YANG Zhifang, ZHANG Hongbo, ZHANG Yurou, LI Tongfang, ZHAO Xiaowei, XUE Chaowei, YE Zhaoxia
		DOI: 10.11660/slfdxb.20231105
		Analyzing the changes and synchronicity in the flood elements of a Loess Plateau watershed is essential for a scientific understanding of the hydrological effects of ecological management in this region, and it is of great significance for improving the level of flood control and disaster reduction. This paper identifies and examines the annual maximum flood events, characteristic elements, and non-consistency characteristics in a river basin based on the hydrograph recession algorithm, through a case study of the Kuye River, a tributary of the Yellow River. We evaluate the changes in intensity of different flood characteristic elements using the constructed range of variability approach for flood characteristic index, and explore synchronization characteristics between these elements using the gray relational degree method. The results show that the flood characteristics take a significant decreasing trend with two-stage variations, and that human activities－such as water and soil conservation measures, land retirement for afforestation and grassland rehabilitation, and coal mining－have synergistically changed the intensity and characteristics of flood events in the basin to a moderate degree. A high degree has remained of the change in the synchronicity of flood peaks, flood volume, and flood frequency; the change rate has grown since 1999. Thus, great attention should be paid to the risk of medium and small floods.
		2023 Vol. 42 (11): 46-58 [Abstract] ( 66 ) PDF (2471 KB) ( 248 )

	59	Study and demonstration of evaporation model for construction areas of floating photovoltaic
		MA Chao, ZHANG Zihao, WU Runze, LIU Shenzhen, LIU Zhao, GOU Haixing
		DOI: 10.11660/slfdxb.20231106
		As a new energy production mode, floating photovoltaic strengthens a comprehensive utilization of water areas, restrains water surface evaporation effectively, and alleviates the current energy crisis and land crisis. However, certain issues emerge in the research of water surface evaporation in the construction areas of floating photovoltaic stations, such as lack of empirical monitoring data, unclear mechanism of water surface evaporation under photovoltaic panels, low accuracy of evaporation calculation models. In this study, data series were collected through in-situ monitoring at the Panji floating photovoltaic station of 150 MW capacity in Huainan, Anhui, including the surface evaporation from natural waters in Mar. 2022 - Jan. 2023, and the under-panel surface evaporation of the photovoltaic modules in Jun. - Nov. 2022. We develop a new whole evaporation model for calculating the construction water area, using an improved Penman model to calculate its surface evaporation from natural waters, and construct an energy-conservation numerical model of under-panel evaporation. And this new model is verified against the in-situ data collected. The results show that the photovoltaic module can reduce under-panel evaporation effectively, and the evaporation model gives R2 greater than 0.85 and RRMSE less than 0.25, quite accurately reflecting the real case under the influence of construction. For a water-saving design of this floating system with its 45% coverage of the water area, calculations reveal its annual water saving is as much as 1.47 million m3 that would generate a remarkable benefit.
		2023 Vol. 42 (11): 59-67 [Abstract] ( 124 ) PDF (1690 KB) ( 222 )

	68	Numerical simulations of unsteady seepage free surface based on improved conservation Level-Set method
		LIU Yuancai, CUI Pengfei, ZHANG Hairong, ZHAO Lanhao
		DOI: 10.11660/slfdxb.20231107
		The problem of unsteady seepage with a free surface is widely recognized in hydraulic engineering and geotechnical engineering. The free surface, usually changing dynamically with time, poses a dynamic boundary problem, and is a key and difficult issue in unsteady seepage research. A free surface numerical simulation method of unsteady seepage based on the finite element method and the improved conservation Level Set method is presented in this paper. We use the Level Set method to capture unsteady seepage free surfaces implicitly on a fixed grid, subtly avoiding the difficulty of other fixed grid methods in handling free surfaces, and have achieved convenient calculations with a good conservation of mass and good accuracy. This paper first discusses the governing equations and numerical discretization of unsteady seepage in porous media, then describes a method for free surface tracking and an algorithm for interpolating physical characteristic parameters. Finally, the accuracy and effectiveness of this new method are verified via several classical examples, such as homogeneous earth dams, heterogeneous rectangular dams, and riverbed seepage problems.
		2023 Vol. 42 (11): 68-77 [Abstract] ( 55 ) PDF (773 KB) ( 187 )

	78	Combinatorial deep learning prediction model for dam seepage pressure considering spatiotemporal correlation
		WANG Xiaoling, ZHU Kaixuan, YU Hongling, CAI Zhijian, WANG Cheng
		DOI: 10.11660/slfdxb.20231108
		Most of the previous studies on the combined prediction of dam seepage pressure are based on a single pressure measurement point for modeling, ignoring the spatiotemporal correlation of multiple measurement points and using a linear combination strategy which suffers problems such as difficulty in capturing nonlinear features between sub-models. This paper constructs a combinatorial deep learning prediction model for dam seepage pressure, considering spatiotemporal correlation. First, the K-nearest neighbor (KNN) is used to optimize the local density function of the density peaks clustering (DPC) algorithm, so as to extract spatiotemporal correlation features from a seepage pressure time series and to achieve adaptive clustering. Then, for the time series, on the basis of its multi-scale refinement by wavelet decomposition (WD), the wavelet neural network (WNN) is used to capture its high-frequency details and construct a highly nonlinear mapping model based on the bidirectional long short-term memory (BiLSTM) for its low-frequency trend characteristics, spatiotemporal characteristics, and external environmental impact factors. Finally, the prediction results of high- and low-frequency feature sequences are combined nonlinearly based on the long short-term memory network (LSTM) to capture the nonlinear characteristics between sub-models. An engineering case analysis shows that our new model raises the prediction accuracy by 75.7% and 41.4%, respectively, compared with the single point prediction model without considering spatiotemporal correlation and the spatiotemporal prediction model using linear combination strategy. This validates its applicability and efficacy as a new approach for dam seepage safety monitoring.
		2023 Vol. 42 (11): 78-91 [Abstract] ( 80 ) PDF (6836 KB) ( 280 )

	92	Multi-view 3D reconstruction and morphological analysis of large particle size rockfill material
		LUO Tao, WANG Zhipeng, ZHANG Tianqi, YI Yu, HUA Cheng, JIN Feng
		DOI: 10.11660/slfdxb.20231109
		Rockfill material is a super large-size aggregate in rock-filled concrete, and the geometry of its particles has a significant effect on its mechanical properties. This study selects randomly 102 rockfill samples with a particle size range of 3 - 40 cm, and constructs a 3D digital model library of rockfill material using a multi-view 3D reconstruction technology. And correlation between the shape parameters of rockfill is discussed with a quantitative analysis and evaluation. The results show this technology can effectively construct a high-precision 3D model for rockfill materials. The main part of the rockfill aggregate in the samples is made up of spherical particles; The specific surface area decreases with the increase in particle size, and falls mostly in the range of 0.08 - 0.2 cm2/g. The value of psephicity decreases with an increase in aspect ratio, and follows a normal distribution as its sphericity does; it features good fractal characteristics with a three-dimensional fractal dimension of 2.01. This study has constructed a useful rockfill model for numerical simulations of rock-filled concrete and in-site evaluation of rockfill geometry.
		2023 Vol. 42 (11): 92-100 [Abstract] ( 67 ) PDF (1421 KB) ( 210 )

	101	Informer-AD dam deformation prediction model integrating multi-dimensional spatiotemporal information
		SU Yan, HUANG Shuxuan, LIN Chuan, LI Yixuan, FU Jiayuan, ZHENG Zhiming
		DOI: 10.11660/slfdxb.20231110
		For the time series prediction issue of dam deformation, a spatiotemporal multi-dimensional input matrix of deformation is derived considering the correlation of deformation at multiple measuring points; an Informer-AD dam deformation prediction model is constructed that integrates multi-dimensional spatiotemporal information based on K-means clustering. We use the K-means clustering to partition rationally the deformation measuring points, then apply a panel data regression model to integrate the analysis of spatiotemporal dimensions and partition results. Finally, we develop an Informer-AD dam deformation prediction model to integrate multi-dimensional spatiotemporal information. This model is used to learn spatial feature sequences and integrate spatial features through a fully connected layer to output predicted dam deformation values. Its application to a concrete gravity dam shows that our prediction method, considering spatiotemporal correlation, can fully explore the relationship of the overall state of dam deformation versus the spatial distribution characteristics of measuring points. It better captures the spatiotemporal characteristics of deformation values and thus improves prediction accuracy, which implies that our model has a high accuracy and satisfactory applicability, useful for engineering application.
		2023 Vol. 42 (11): 101-113 [Abstract] ( 141 ) PDF (2759 KB) ( 347 )

	114	Simulations of ground motions based on Code for Seismic Design of Hydraulic Structures and study on reliability of high core wall dam
		LU Yunzhu, PANG Rui, JI Rui, XU Bin
		DOI: 10.11660/slfdxb.20231111
		Seismic reliability evaluation often gives an effective estimate of the dam safety under seismic load. Recently extensive attention has been attracted by the combination of applying artificially simulated stochastic ground motions and quantifying the stochastic responses of structures. Based on Code for Seismic Design of Hydraulic Structures, this paper develops a non-stationary stochastic ground motion model for five different sites using a speactral expression-random function method. Then, we combine this model with a generalized probability density evolution method to extract probability information from dam dynamic responses. Finally, we borrow the idea of equivalent extreme value event, and construct a framework for dynamic reliability analysis of high core wall dams, with dam deformation and dam slope slip used as evaluation indexes. A case study of the Rumei Dam shows that the calculations of dam deformation failure probability are higher than those of the dam slope slip failure. Our analysis framework considers load randomness and gives more accurate failure probability, thus laying a basis for the seismic reliability evaluation of high core wall dams and its application in geotechnical engineering.
		2023 Vol. 42 (11): 114-125 [Abstract] ( 55 ) PDF (2348 KB) ( 255 )

	126	Experimental study on crack self-healing of hydraulic concrete with mixed microorganisms
		MENG Yongdong, WANG Yu, XU Xiaowei, DING Yi, CAI Zhenglong, TIAN Bin
		DOI: 10.11660/slfdxb.20231112
		Hydraulic concrete is prone to erosion and cracking in the wet-dry alternating zones under water level fluctuation. A mixture of aerobic and facultative anaerobic mineralized microorganisms can better adapt to the fluctuating oxygen content in concrete cracks. This study prepares a microbial self-healing agents using the microorganisms of aerobic Bacillus megaterium and facultative anaerobic Sporosarcina pasteurii for two cases－a single component and a mixture of both, and conducts the compressive strength test and permeability coefficient test on the concrete samples. For each case, we evaluate the influence on the performance and crack self-healing effect of hydraulic concrete through a quantitative index analysis of crack repair. An optimal mixing ratio of aerobic and facultative anaerobic has been obtained. A SEM analysis of the samples is used to reveal the microscopic mechanism of mixed microorganisms on concrete performance improvement. The results show that the mixing of mineralized microorganisms can effectively elevate the density of concrete pore structure, and the best effect of calcium carbonate precipitation occurs at the mix ratio 4:6 of Bacillus megaterium and Sporosarcina pasteurii. Under the synergistic influence of aerobic microbial respiration and facultative anaerobic microbial enzymatic action, the mixed microorganisms yield better improvement on the mechanical properties and crack self-healing efficiency of hydraulic concrete.
		2023 Vol. 42 (11): 126-135 [Abstract] ( 127 ) PDF (2089 KB) ( 278 )

	136	Dam deformation prediction model based on singular spectrum analysis and improved whale optimization algorithm-optimized BP neural network
		WANG Haoran, NIU Xinqiang, XU Lifu, YAN Tianyou, ZHU Yantao
		DOI: 10.11660/slfdxb.20231113
		Deformation is a comprehensive reflection of the safety state of a dam; To ensure its long-term service, a significant task is to develop a reliable prediction model between its deformation and environmental variables. Previous models are easily affected by the noises in data sets or structural parameters, and often fall into local extremum or overfitting. To improve the accuracy and generalization ability of the model, this paper presents a back propagation (BP) neural network method based on the singular spectrum analysis (SSA) and an improved whale optimization algorithm (IWOA). The method uses SSA to filter out noises in the raw data, and extracts feature components from the dam deformation time series. Then, an IWOA-optimized BP neural network is used to explore the complicated nonlinear relationship between the denoised data and environmental variables. Practical applications to the Bailianya arch dam show that in comparison with the traditional optimization algorithm, SSA can eliminate the outliers effectively from the raw data, and the BP neural network optimized by IWOA is much better in accuracy and stability, both applicable to the analysis and prediction of dam deformation monitoring data.
		2023 Vol. 42 (11): 136-145 [Abstract] ( 128 ) PDF (2178 KB) ( 309 )

	146	ARIMA-LSTM time series probability prediction method for simulation parameters of high arch dam construction
		GUAN Tao, CHEN Purui, YU Hao
		DOI: 10.11660/slfdxb.20231114
		Most previous studies for updating the simulation parameters of high arch dam construction are based on probability prediction alone or point-wise prediction considering their time series characteristics; such methods are usually faced with difficulty in quantitative description of their randomness while considering their time series characteristics. To couple both factors, this study uses an Autoregressive Integrated Moving Average model (ARIMA) to predict the probability along with consideration of parameter time series characteristics, and develops a new intelligent updating model of the simulation parameters of high arch dam construction based on ARIMA and a Long Short-Term Memory model (LSTM) that can learn the advantages of complex nonlinear characteristics of parameter time series. This new model uses ARIMA to predict the linear part of the parameter time series, and uses LSTM to train and predict the residuals output by the ARIMA model. We fuse the predicted linear part and the nonlinear part of the predicted residuals, and then make probability predictions at the 95 % confidence interval to obtain the final result. Thus, we can calculate the construction simulation parameters that describe both randomness and temporal characteristics, and have achieved a new method of higher accuracy (with MSE of 0.518, MAE of 0.519 and RMSE of 0.720) than the model of ARIMA, ARIMA-BP neural network, or random forest (RF). Compared with traditional simulations, the simulation results of a high arch dam construction system are greatly improved using the simulation parameters predicted by our new method.
		2023 Vol. 42 (11): 146-156 [Abstract] ( 111 ) PDF (2538 KB) ( 202 )