Journal of Hydroelectric Engineering

水力发电学报

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(vi)Chinese Academic Journals (CD).


2023 Vol. 42, No. 5 Published: 2023-05-25

	1	Extraction of flow resistance characteristic parameters and SVM-assisted riverbed morphology identification ^Hot!
		BAI Yuchuan, SUN Yanjie, SONG Xiaolong, XU Haijue
		DOI: 10.11660/slfdxb.20230501
		Sediment transport and flow resistance are affected by riverbed morphology. As an important aspect of riverbed evolution, effective predictions of bed form changes are practically significant for river regulation and sediment research. This paper determines the characteristic parameters of riverbed forms based on the law of flow resistance. We find an ‘S’-shaped relationship exists between the characteristic parameters of the flow and bed form through analysis of previous experimental data. By automatic division of the bed forms using a SVM multi-classification method, we obtain a fitting function from the nonlinear and linear fittings of dimensionless characteristic parameters, and determine the criterion of riverbed forms through a derivative analysis of the fitting function of characteristic parameters and the sand wave forms. Finally, this criterion is verified against the previous laboratory experimental data and in-situ measurements in literature, showing the method is feasible and quite accurate in riverbed shape recognition.
		2023 Vol. 42 (5): 1-9 [Abstract] ( 132 ) PDF (3175 KB) ( 459 )

	10	Impacts of hydropeaking on riparian habitats and macroinvertebrates community structures
		WANG Haoran, WEN Jiaqi, LI Chong, JIA Zeyu, CHEN Yongcan, LIU Zhaowei
		DOI: 10.11660/slfdxb.20230502
		The main objective of this study is to quantify the impacts of peaking flows on riparian habitat fragmentation and macroinvertebrate assemblage variations. Field surveys of the Mudan River were conducted in the autumn of 2014. We sampled its macroinvertebrate assemblages and collected the habitat variables in six selected drawdown pools in its two regulated reaches, and made comparison with the data collected in its natural channels. Redundant analysis (RDA) of the physical and chemical habitat variables is used to summarize total variations in the habitat data and to identify the major environmental gradients in the pools. Results indicate substantial differences in the habitat variables and macroinvertebrate assemblages between the pools and river channels. In the pools, dissolved oxygen and pH were significantly higher than the natural river; higher levels of ammonia nitrogen and total nitrogen were found; and diptera, Hemipteran, Gastropoda were dominant. The abundance, taxa richness, Shannon-Wiener index, and Margalef richness significantly decreased in the pools due to habitat isolation. Redundancy Analysis reveals that the key habitat variables affecting macroinvertebrate assemblages in the pools are dissolved oxygen, pH and total phosphorus.
		2023 Vol. 42 (5): 10-16 [Abstract] ( 154 ) PDF (895 KB) ( 349 )

	17	Temporal and spatial distributions of impact pressure on slope-deck structure under dam-break waves
		CHEN Cheng, CHEN Haoyan, DENG Xin
		DOI: 10.11660/slfdxb.20230503
		Dam-break accidents occur at times at hydropower stations; dam-break waves often impose a significant impact on the downstream structures. In this work, physical model tests are carried out on a flume to record the mechanical characterization of a slope-deck structure, located at the end of the flume that is impacted by the dam-break waves created by a dam-break wave generating system (DWGS). We focus on analysis of the load history and distribution characteristics of uplift pressure on the structure during wave impacting. In time variation, the impact pressure shows five successive stages; the two stages of wave front impact pressure and quasi-constant pressure are examined in this paper. In spatial distribution, these two pressures decrease along the stream from the head to tail of the deck section, while they change little in the cross-flow direction. Based on the test data, a formula for calculating the platform uplift pressure distribution is fitted, and streamwise variations in the peak values of wavefront impact pressure and quasi-constant pressure are obtained.
		2023 Vol. 42 (5): 17-24 [Abstract] ( 107 ) PDF (2310 KB) ( 192 )

	25	Sequential power and energy balance practical method for hydro-thermal-wind-solar power systems
		CHEN Dian, LU Runzhao, ZHANG Jian, HUANG He, CAI Chao, ZHANG Yantao
		DOI: 10.11660/slfdxb.20230504
		In the context of new-type power systems, their proportion of new energy is increasing gradually. Analysis of the balance of an electric power and energy system faces new challenges because of randomness and fluctuation in new energy generation and its reverse peak regulation. This paper combines the strategic needs of energy transition to develop a new power balance mode for such a power system and demonstrates the key workflow of its power balance. Then, we conduct detailed modeling of its thermal power, hydro-power, pumped storage/storage power supply, and construct a sequential power balance model. Finally, a case study of a real power grid is used to verify this new method. We show that it reflects the characteristics of power sources accurately, gives the conditions of new energy consumption, and supports the study of power system lifting measures, thus significantly raising the level of scientific planning of the power systems.
		2023 Vol. 42 (5): 25-34 [Abstract] ( 91 ) PDF (1077 KB) ( 409 )

	35	Effect of sea waves on radiant energy of floating photovoltaic
		LU Wenhe, LIAN Jijian, DONG Xiaofeng, LIU Run
		DOI: 10.11660/slfdxb.20230505
		Offshore Floating Photo Voltaic (FPV) is an effective way to deal with the contradiction between photovoltaic development and land resources. However, under the action of sea waves, the photovoltaic panels on a FPV structure always oscillate with wave motion, which makes their angles to the sun change constantly and imposes a great impact on power generation. In this paper, a formula is derived for calculating the radiation energy of the panels under regular sea waves, and the concept of time-equal-dip angle is summarized and introduced. Using the Bohai Bay conditions of regular waves at the same latitude, efficiency ratios are calculated for the irradiated energy between the offshore photovoltaic panels with different dip angles and the onshore ones with the best dip angles. From the calculations, we find that for the panels with different dip angles, variations in the radiation energy under sea waves follow basically the same trend－it decreases with the increase in the average dip angle. A recommended standard of the panel motion amplitude is given as a design criterion useful for estimating hydrodynamic responses in the development of FPV structure.
		2023 Vol. 42 (5): 35-42 [Abstract] ( 222 ) PDF (892 KB) ( 496 )

	43	Deep learning runoff prediction model based on multi-source data fusion
		ZHOU Qingzi, HE Zili, WU Lei, MA Xiaoyi
		DOI: 10.11660/slfdxb.20230506
		To explore the effect of deep learning algorithms combined with the multi-source data fusion method in watershed runoff prediction, a bidirectional Long Short-Term Memory (LSTM) neural network model and a data fusion algorithm of the ensemble Kalman filter are combined to construct runoff prediction models for five watersheds in the upper Hanjiang River. These models are verified using long-series hydrometeorological datasets from the study area and atmospheric circulation factor datasets. The results show that in the same prediction period, the models improve the prediction indexes and better capture the extreme values of runoff series in comparison with the traditional LSTM model. After the data fusion algorithm is used to join the atmospheric circulation factor datasets, the evaluation indexes of different watersheds can be further improved, and their time variations are more stable with a longer forecasting period. These prediction models are effective in improving deep learning-based runoff predictions.
		2023 Vol. 42 (5): 43-52 [Abstract] ( 225 ) PDF (3069 KB) ( 1022 )

	53	Uncertainty analysis of runoff generation and concentration parameters under urbanization and simulation of flooding and waterlogging. Case study of Jincun District, Jincheng
		SHU Xinyi, XU Zongxue, YE Chenlei, LIAO Ruting
		DOI: 10.11660/slfdxb.20230507
		Identifying sensitive parameters and reducing model uncertainty is a key step to improve urban flood simulation accuracy. This paper constructs an urban flood simulation model for the Jincun District of Jincheng City, identifies sensitivity parameters using a local method and a global method separately, and examines parameter sensitivity for different land surface conditions. Based on sensitivity analysis, flooding and waterlogging characteristics under different land surface conditions are simulated for different rainfall scenarios and urbanization processes. The results show that the puddle storage in impervious areas is more sensitive. The sensitivity of maximum infiltration rate is increased locally and decreased globally as the rainfall return period increases. Parameters related to impervious surfaces are more sensitive in the areas with more hardened ground surfaces. Flooding and waterlogging in different areas are more severe with a longer return period. Under the same return period, waterlogging is more concentrated under the condition of urbanization. This study reveals parameter uncertainties and flooding and waterlogging characteristic responses for application of the urban distributed rainfall-flood model and urban resilience enhancement.
		2023 Vol. 42 (5): 53-66 [Abstract] ( 99 ) PDF (6439 KB) ( 327 )

	67	Development and application of intelligent axis adjustment system for vertical hydro-generator units
		XU Bo, ZHANG Chunhui, LI Youping, REN Jishun
		DOI: 10.11660/slfdxb.20230508
		To date, the axis adjustment of a vertical hydro-generator unit in China and abroad has adopted the method of constant-phase fixed-point turning, manual table measurement, and manual calculation adjustment. Not only does this require a long turning cycle and a heavy workload of manual measurement and calculation, but its work efficiency and the accuracy of calculation and adjustment are relatively low due to the lack of sufficient measurement points and strong subjectivity in adjustment schemes. This paper develops an intelligent axis adjustment system for vertical hydro-generator units. By using the continuous turning axis adjustment process, we have achieved automatic data collection, wireless communication, and automatic calculation and analysis of the turning data. This system can generate axis adjustment schemes automatically. Its successful application at the Gezhouba hydropower station proves that it can greatly raise the intelligent level of axis adjustment process.
		2023 Vol. 42 (5): 67-76 [Abstract] ( 102 ) PDF (1648 KB) ( 156 )

	77	Analysis of GD² estimation formulas and statistical formula fit for hydro-generator units
		MA Duo, XIE Yonglan, ZHANG Junzhi, LIU Jianhua
		DOI: 10.11660/slfdxb.20230509
		The moment of inertia plays a key role in the stability of a hydro-generator unit and the stability of the power system connected. For a hydro-generator unit, it is a key index for measuring its fast reaction performance and imposes a very significant influence on its stability; in engineering, the flywheel moment GD² is usually used to characterize its moment of inertia. In the design stages of pre-feasibility and feasibility studies, the GD² value is usually determined by estimation. This paper presents an analysis of the estimation formulas of two types commonly used for GD² estimation based on typical engineering cases and a large body of statistical data, and discusses how to choose reasonable GD² values. We use GD² data samples from the real hydropower units, which have been put into operation in China in recent years, to fit statistical formulas so as to lay a basis for calculation of the GD² of a hydro-generator unit in the initial design stage.
		2023 Vol. 42 (5): 77-85 [Abstract] ( 94 ) PDF (1707 KB) ( 145 )

	86	Analysis of pressure fluctuation and internal flow characteristics of axial flow pumps under off design conditions
		WANG Kaijie, ZHAO Yong, WANG Shenghui, XIAO Yexiang, WANG Chengpeng, ZHANG Jin
		DOI: 10.11660/slfdxb.20230510
		When a running axial flow pump deviates from its design condition, especially at very low flows, strong vortices in its flow channel will cause severe pressure pulsation, deteriorating the safe and stable operation of the pump unit. In this paper, by examining a high specific speed axial flow pump as a case study, we simulate numerically three-dimensional flows in its whole flow channel for five operating points by using a code equipped with a RNG k-ε turbulence model, and discuss its unsteady flow characteristics. Its external characteristic curves at five steady operating points are verified, and we find they agree well with the design performances. We calculate the unsteady cases at two flows of 0.2Qd and 1.0 Qd. The results show obvious differences occur between the two conditions in the amplitude and frequency of pressure pulsation mixing at the same measuring points. At the flow of 0.2Qd, the maximum peak-to-peak value of pressure pulsation is four times higher than that at 1.0 Qd, indicating highly uneven flow patterns in the channel. In unsteady cases, the dynamic and static interference, usually causing low-frequency pulsation, will greatly enhance the 3fn and 5fn pulsation amplitudes of its principal frequencies. At the measuring points closer to the impeller and guide vanes, these increases are more significant, and the flows are more sensitive to the interference, with the pulsation amplitudes of 3-5 times that of 1.0 Qd; the influence in the runner and guide vane section is stronger. Thus, pressure fluctuation at the lower flow is more severe, and the impeller and guide vane sections are more affected by the static and dynamic interference, so that severe flow separation and more vortices occur in the guide vane channels.
		2023 Vol. 42 (5): 86-96 [Abstract] ( 110 ) PDF (5066 KB) ( 264 )

	97	Sand piping tests and hydraulic condition analysis with particle shape effect
		KANG Jie, REN Jie, NAN Shenghao, GUO Hengle, ZHANG Jinjin
		DOI: 10.11660/slfdxb.20230511
		Soil piping is one of the main causes of instability and failure in hydraulic engineering. To date, most of the existing studies on soil piping ignored the shape effect of sand particles; The seepage deformation results of some real projects are inconsistent with the theoretical criterion. In this work, a self-designed soil permeability test device is used to conduct piping tests on a variety of soils that have different gradings and three different particle shapes. Based on the fractal theory and a capillary model, critical hydraulic conditions for soil piping are analyzed at multiple scales, and the testing process is monitored using an acoustic emission system. The results show that sand samples with spherical particle shape have relatively high permeability, prone to piping failure; with the increase of blocking particles, the soil tends to be stable under the action of seepage. The critical hydraulic gradient of soil piping is inversely proportional to the mass fractal dimension; the capillary model based on particle shape and effective pore volume predicts the critical flow rate of piping with a satisfactory accuracy. The evolution patterns of acoustic emission cumulative energy can reflect the characteristics of soil piping development.
		2023 Vol. 42 (5): 97-106 [Abstract] ( 108 ) PDF (2229 KB) ( 249 )

	107	Integrated learning fusion model for seepage safety monitoring of rockfill dams
		SONG Jintao, YUAN Shuai, LIU Yunhe, YANG Jie
		DOI: 10.11660/slfdxb.20230512
		The seepage monitoring model of rockfill dams is a key factor for quantitative analysis of seepage safety. Most of the traditional models adopt a statistical model or machine learning intelligent algorithm model separately, unable to effectively integrate the advantages of both. This paper presents an innovative integration of statistical models with multiple parallel intelligent algorithm prediction models in the framework of integrated learning, and uses the interpretability of statistical models and the high adaptability of fit of intelligent algorithms to improve the prediction accuracy of this integrated model. First, we fully consider the lag effect of seepage influence factors on the basis of the classical seepage statistical model, and improve the expression for the water level factor and the rainfall factor. Then, based on the integration principle of differential evolution adaptive Metropolis (DREAMZS), several advanced intelligent algorithms and improved statistical models in machine learning are integrated, and optimal weight coefficients are obtained for each model. Case analysis shows that in comparison with the single statistical model or the intelligent algorithm model, our integrated learning fusion model improves prediction accuracy significantly and can integrate effectively the advantages of a statistical model and multiple intelligent models, providing a new modeling method for dam seepage monitoring.
		2023 Vol. 42 (5): 107-119 [Abstract] ( 183 ) PDF (615 KB) ( 494 )

	120	Rockfill dam deformation prediction model based on deep learning-extracted spatiotemporal features
		CHEN Ying, MA Gang, ZHOU Wei, WU Jiye, ZOU Quancheng
		DOI: 10.11660/slfdxb.20230513
		Previous models for intelligent prediction of rockfill dam deformation, lacking attention to the uneven distribution of deformation time series over multiple measuring points, are limited to low accuracy. This paper develops a rockfill dam deformation prediction model, CTSA-ConvLSTM, to combine a convolutional neural network (CNN), the attention mechanism, and a long short-term memory (LSTM) neural network. This model extracts the temporal and spatial characteristics of deformation and generates different weights for the measurements taken at different instants and different locations, so that it realizes the adaptive learning of global deformation patterns of a rockfill dam. In the case study of the Shuibuya dam, the model is verified against the deformation data from all the measuring points at the maximum dam section. It performs better than Holt-Winters and other conventional time series prediction models, and its prediction accuracy is higher than that of a LSTM-based deformation model developed by the authors. By extracting the spatiotemporal characteristics of monitoring data through deep learning, it improves the accuracy and provides a new idea for improving dam safety monitoring models.
		2023 Vol. 42 (5): 120-132 [Abstract] ( 226 ) PDF (2095 KB) ( 533 )