Cascade hydropower development disrupts the continuity of natural river water temperature, leading to a significant cumulative effect on the temperature and a series of ecological effects. This study examines the water temperature along the lower Jinsha River and in the Three Gorges reservoir area , and reveals its spatial and temporal variations on different scales before and after dam construction and the cumulative effects. The impact of water temperature changes on fish spawning is also discussed. The results show that after the Xiluodu and Xiangjiaba dams were constructed, the annual mean water temperature difference along the lower Jinsha becomes smaller; along the river, the annual highest temperature at the hydrological stations shows a decreasing trend, while the annual lowest is significantly elevated, especially in January and December. After the construction, the annual temperature variation is reduced, and the time period featuring water temperature distribution has a trend of ‘convergence’－the days of water temperature distribution changed from M-type to V-type. An examination on the cumulative effect indicator finds that after dam construction, a significant time crowding effect occurs－a lag time in the extreme water temperature, water temperature delayed up to 1 - 2 months, the fish of 48% and 44% affected to a high degree at the Panzhihua section and the section downstream of Xiangjiaba dam respectively－thereby affecting severely the fish spawning and reproduction in the downstream and the fish protection sections. The study demonstrates the cumulative effects of water temperature impacted by cascade dam construction and its impact on fish spawning, laying a basis to enhance the role of temperature changes caused by large-scale cascade dams and the downstream ecological restoration.

The deformation and stability analysis of high steep slopes is a key technical problem in the construction and operation of hydropower projects. Over the past two decades, China has built a large number of large-scale water conservancy and hydropower projects. Many key technical problems of high dams and large reservoirs have been solved successfully, and remarkable progress has been achieved in the life cycle performance evolution and safety control of high, steep slopes of reservoirs. This paper takes the performance evaluation of high steep slopes in the southwest hydropower projects as the main research line, and focuses on the deformation and stability evolution of high steep slopes. We examine the research progress in determining the influencing factors of stability and failure modes of high steep slopes, stability evaluation and deformation analysis methods, seepage analysis, and safety control. The latest researches are discussed in detail on the strict three-dimensional limit equilibrium method, modified Hoek-Bray wedge method, rigid body spring method, parameter inversion method based on monitoring data, and slope seepage analysis. We also discuss the academic thinking and technical route and certain future challenges to the life-cycle deformation and stability evolution analysis of high steep slopes in hydropower engineering.

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.

The efficiency of water energy utilization of a conventional fixed-speed small hydropower unit is often very low when its operation deviates from the design conditions. However, by examining the comprehensive characteristic curves of the turbine, a law that for a unit under the optimal guide vane opening and a certain optimal unit speed achieves the highest efficiency of water energy utilization has been found. And, a variable-speed control strategy was proposed to achieve such an efficiency, particularly for the condition of significant water head variations. This paper presents a mathematical model for simulations of a variable-speed small hydropower unit, incorporating the operation of the turbine, governor, generator and converter. A closed-loop variable-speed control system based on this new strategy was designed. Numerical simulation results indicate that compared to the fixed-speed operation scheme, the new scheme achieves a 2.39% increase in power output at the water head of 28 m. Field tests at the Zangtanqiao hydropower station verify that the small hydropower unit is successful in operating under its optimal guide vane opening and optimal rotational speed, and that the new control strategy improves power generation efficiency across high, medium, and low head conditions. The new strategy is easy to apply in the industry of small hydropower and of great significance to the real hydropower projects.

This paper focuses on the key issue of the Three Gorges hydropower station’s in-plant economic operation, which is aimed at achieving a real-time load allocation of large-scale units for minimizing water consumption. Dynamic programming usually encounters the curse of dimensionality when dealing with a large-scale hydropower unit cluster, and therefore, it cannot meet the requirement of real-time dispatching decision for the station. For training a multi-period unit load distribution model and its decision-making, we develop a deep reinforcement learning-based framework to train the deep neural network and generates unit load distribution plans through a pre-trained network model. We apply a group theory idea to processing the state and action features of the learning, so as to compress the state and action space significantly and improve model training efficiency. The results indicate that compared to dynamic programming, our new method shortens the decision-making time by two orders of magnitude at a cost of less than 1% benefit loss. Thus, it offers a rapid and efficient solution for the unit load allocations in large-scale hydropower stations.

How to generate a slip surface is one of the key issues in slope stability analysis, and to construct a slip surface with a complex shape is quite challenging. The curve based on the integral of a Logistic function or other basis functions can take the place of the traditional slip surface generated using non-circular curves, which not only improves the smoothness of the slip surface but reduces its degree of freedom. Slip surface generating methods based on basis functions are sorted out in this paper. They are divided into the superposition method of continuous functions and the integral operation method. The characteristics of the Logistic function and its integral are examined carefully, and the influence of different parameters on the curve shape is obtained to narrow parameter ranges. This provides a basis to reduce the degree of freedom of slip surfaces. And a method is presented to determine the initial parameter values of a given slip surface. The calculations of two slope examples show our method is tolerably good in slope stability analysis.

To explore the effect of fiber on the self-healing effect of microbial concrete cracks, polyacrylonitrile fiber is selected and incorporated into microbial concrete; an experiment is conducted to test the mechanical properties of microbial concrete blended with fiber, its crack permeability, and the residual crack width during its self-healing cure. The results show the concrete specimen with a fiber content of 1.5 kg/m3 has a splitting tensile strength that recovers to 95.1% of that of the specimen without microbial carrier. After 28 days, the permeability coefficient of the specimen with an average crack width of 0.43 mm is lowered from 3.35×10-5 m/s to 3.40×10-6 m/s, or a relative decrease of 89.9%. The maximum width of cracks that can self heal is 0.82 mm. The incorporation of fiber into microbial self-healing concrete effectively improves its crack resistance and enhances the self-healing effect of cracks.

The exhaust air pressurization process of a pump-turbine during the shift from this condition is explored to address the issue of instability that is easy to generate in pumped storage units during phase transition from power generation to power generation. Unsteady multiphase flows in a prototype pump-turbine are simulated using the SST k-w model and VOF model. The simulations demonstrate the transient variation trends in the unit's active power, exhaust rate, vaneless zone pressure, and the relationship of the evolution of internal flows in the pump-turbine versus its external characteristics. They also reveal the dynamic distribution of air and liquid phases in the unit in exhaust pressurization process. The study demonstrates that in this process, the unit's active power absorption from the grid and the exhaust rate decreases gradually after their initial rapid decreases, its pressurization effect is evident, and its pressure pulsation frequencies in the vaneless zone are higher. In the vaneless zone, a high-speed water ring forms gradually driven by the inflow coming from tail water, so that air-liquid movements are chaotic and the air is prone to flowing backward into the draft tube, which has a significant impact on the unit's stability. The findings in this study are useful for improving the stability and safety of pumped storage units.

This paper describes a new model based on the microeconomic principle of diminishing marginal productivity during the production process to economically and reasonably exploit the regulatory capacity of hydropower station reservoirs and to facilitate the efficient integration of wind and solar resources. This model considers the regulating capability of the reservoirs as a variable factor, treating the efficient consumption of wind and solar resources as a "fixed factor". It aims at maximizing the benefits of hydro-wind-solar co-generation and can be solved to obtain optimized operation plans for the reservoirs to facilitate the economic consumption of wind and solar resources. Case analysis demonstrates that under the given condition of inflow, load demand, reservoir operation status, and other factors, the model effectively reflects the reservoir’s contribution to the economic consumption and the co-generation benefits during its participation in wind and solar output regulation. Optimized operation scheme is given for the reservoirs involved in wind and solar output regulation under different scenarios. The model helps leverage fully the regulating capacity of the reservoirs and enhance the co-generation benefits while ensuring the safety of power systems.

As global climate change intensifies and extreme events occur more frequently, the safe and stable operation of hydro-wind-solar multi-energy complementary systems faces great challenges, necessitating a rational evaluation of the resilience of these systems under extreme event disturbances. This study first elaborates on the complementary characteristics of hydropower, wind, and solar energy, and examines the types of disturbances confronted by these integrated systems. Then, a new concept of resilience for a complementary system is discussed, and a three-stage conceptual model for resilience is developed. Based on this framework, we select monthly, seasonal, and annual timescales and specify power output thresholds for the complementary system, and formulate an evaluation index and methodology for resilience assessment based on power output loss. Finally, to evaluate the resilience, a case study is conducted on a clean energy base in the upper Yellow River. The assessment results validate the effectiveness of the evaluation index and methodology. Notably, hydropower energy demonstrates superior resilience compared to wind and solar power, and the system’s overall resilience is enhanced significantly through utilizing the complementary nature of hydropower, wind, and solar energy. This study helps decision-making for resource allocation, scheduling strategies, and safe operation of integrated hydro-wind-solar energy systems.

A general segmentation model for dam concrete surface cracks in images often faces a shortage of training data due to the high cost of manual annotation, resulting in insufficient accuracy in its results. This paper presents an automatic annotation and segmentation algorithm that integrates image feature extraction and deep learning techniques. The algorithm first adopts a strategy for combining binarization and edge detection to annotate unlabeled crack defects automatically, and constructs a large-scale dataset of 19,101 crack masks. Then, a hybrid model for combining Swin-Transformer and Unet (Swin-Unet) is designed by introducing the hierarchical attention mechanism of Swin-Transformer into the Unet architecture. Finally, the model is validated through experiments and result analyses on the self-constructed datasets. The results show this Swin-Unet model achieves the highest crack classification accuracy (100%) and a segmentation IoU of 93.1% or 7.5% improvement over the Unet segmentation model (85.6%). This indicates the introduction of the Swin-Transformer architecture enhances the model's capability of associating global and local features, significantly improving the crack defect segmentation accuracy. Besides, an analysis of the minimum enclosing rectangle of cracks reveals significant clustering in both the direction and shape distribution of cracks, deepening our understanding of the mechanisms of crack formation and useful for predicting crack propagation direction.

Hydraulic machineries running on muddy rivers suffer from sediment erosion that often causes deformation and damage to flow components, seriously threatening the safe operation of hydropower plants. This study conducts an experiment on a rotating jet facility to examine the influence of sediment concentration on the impact abrasion and erosion characteristics of hydraulic machinery materials, using the weight loss method and Scanning Electron Miscroscopy (SEM). The results show the cumulative weight loss of materials from impact abrasion and erosion takes a nearly linear increase with the rise of sediment concentration. With sediment concentration lower than 60 kg/m3, the increase in the cumulative weight loss of the tested three material is gradual; it becomes rapid at higher concentration. As the concentration increases, the impact erosion causes more severe damage to the materials, but cavitation erosion shows a weakening trend. The higher the surface hardness of the material, the better its resistance to cavitation erosion, and the lower the critical sediment concentration at which the weights of abrasion and erosion are equal. Flow cavitation affects the angle at which sand particles impact on the material surface, and it usually increases the horizontal impact load of the particles. The abrasion marks show lip edges and material accumulation, and the sand particle cutting damage dominates at higher sediment concentrations.

Once encountering extreme rainfall, a check dam is highly prone to flooding and collapse, posing a grave threat to downstream communities; a quantitative risk assessment of dam failure caused by its breaching is urgent. Previous studies overlooked the impact of sedimentation on dam failure. In this paper, we examine the influence of rainfall-induced sedimentation on the failure and reveal a mechanism of the dam behavior under the interaction of rainfall, flooding, and sedimentation. We then develop a mathematical model for quantitative assessment of dam failure risks, extending traditional "rainfall-flooding" modeling to "rainfall-flooding-sedimentation" so as to achieve more accurate assessment of dam failure and its risks. We have applied this model in two failure cases of the Wangmaogou watershed and assessed their risks under different return periods of rainfall. Results indicate that the impact of sedimentation is a significant factor. For the Guandigou 3rd dam, Beitagou dam, and Kanghegou 3rd dam, the judgement and calculation results agree with all the three real failure cases when using the simulation considering the sedimentation factor, while they deviate from the reality of the Beitagou dam failure when neglecting this factor. Detailed comparison indicates that under the rainfall on July 26, 2017, neglecting the sedimentation role advances the failure initiation time of the three dams as listed above by 11 minutes, 13 minutes, and 7 minutes respectively. This facilitates timely issuing of the warnings of emergency and quick evacuation of the people living around the dam, and enhances the accuracy of check dam warnings. For the Guandigou 3rd dam under the rainfalls of different return periods, its failure discharge is 4.82 m3/s and 5.97 m3/s under 10-year and 200-year periods respectively. The other two dams can withstand a 200-year return period rainfall under current siltation conditions.

High-altitude wind energy is an important potential resource in the future. This paper presents a statistical analysis of the wind characteristics in China at different above ground levels below 4000 m, based on the ERA5 wind data from the European Centre for Medium-Range Weather Forecasts and digital elevation data, focusing on the spatiotemporal distributions of wind speeds and the frequencies of typical wind speeds. The results show a complicated spatial pattern of wind speeds over China, i.e., higher wind speeds in the northeastern and plateau regions, and lower wind speeds in the southern and northwestern regions. At low levels such as 100 m, coastal winds are high, while inland winds are low. Seasonal variations in wind feature higher speeds in spring and winter and the lowest in summer. The highest wind speeds occur typically in January and December, while the lowest typically in July and August. Wind power density is generally higher in the northeast, southwest and coastal regions. At low levels, the regions that feature relatively high frequencies of extreme high wind speeds appear in Inner Mongolia and the northeastern provinces; at high levels, they appear in Tibet and Qinghai. This paper also gives the maps and variation patterns of wind resources at different above ground levels, which are helpful for the planning and design of airborne wind power projects.

A vertical pipe inlet-and-outlet is a common inlet-and-outlet type for controlling water flow at both ends of a pumped storage power station waterway system. It is a passage of bidirectional flows that undergoes two 90° turns (horizontal-vertical-horizontal) within a short distance. In such an inlet-an-outlet, flow direction changes drastically and its hydraulic conditions are complicated. For the design and construction of pumped storage power plants, it is of great significance to conduct an in-depth study of its hydraulic characteristics and to optimize its structure and shape. This paper first discusses the application background of the vertical pipe inlet-and-outlet and the characteristics of its channel shape, and examines the problems with its hydraulic characteristics. Then, we present a summary on the recent progress in the research on this device, the distribution of flow velocity in its trash rack section, the vortices and its head loss, etc. Finally, we sum up the research methods for its hydraulic characteristics, and suggest certain directions to focus on in the future research.

The design rule curves are used to simulate reservoir operation to verify the design values and obtain a benchmark for comparative study. Joint and multi-objective optimal operation models are developed for the lower Jinsha-Three Gorges-Gezhouba cascade reservoirs. We solve the optimal model and its Pareto frontier using a parameterization-simulation-optimization framework based on the Gaussian radial basis functions and a Borg many-objective evolutionary algorithm. Comparative study reveals that the design values of annual hydropower generation are reasonable and reliable, and the joint operation of cascade reservoirs must consider the backwater effect of downstream reservoirs. Compared to the rule curve scheme, the joint (optimal) operation scheme reduces water spillage by 20.38% (30.44%) and increases annual hydropower generation by 4.01% (5.45%), with significant increases in the generation by the reservoirs of Xiluodu, Xiangjiaba, and Gezhouba. Generally, a conflict between hydropower generation and impoundment efficiency occurs, and the Three Gorges Reservoir will be faced with a challenge in dry years if it starts impoundment on the tenth of September. We suggest that the Three Gorges Reservoir should optimize further its flood-limited water level and consider an earlier start of impoundment.

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.

For concrete dam construction, traditional computer vision target recognition methods are difficult to meet the requirements for intelligent detection in complex construction sites, as it involves narrow spaces, continuous process transitions, and various other elements such as personnel, machinery, and environment (human-machine-environment or HME). These elements often lead to occlusions, dense overlaps, and variations in size and orientation. This paper describes a new method, YOLOv5-SS, for recognition of the multiple elements in the HME scenarios of such construction. By integrating a CBAM attention module, this method improves the performance of the object detector and enhances its sensitivity to HME elements of different sizes and positions. And, it incorporates the weighted bidirectional feature pyramid network (BiFPN) to enable the object detector to focus on key image information related to real-time HME elements. To validate the recognition capability of this method, a dataset based on image information from a concrete arch dam construction site is used. Comparison of YOLOv5-SS with the YOLOv5 and Faster R-CNN models demonstrates it effectively improves the efficiency and accuracy of target detection in concrete dam construction scenarios.

As the city expands, the river shoreline is constantly being encroached upon, and the river's flow capacity and water environment are damaged severely. Therefore, efficient methods for monitoring complicated river courses are urgently needed. This paper precents a new method for collecting images and data of a river and its banks using drones and enhancing data with the Generative Adversarial Network. We identify and locate five kinds of typical foreign bodies on the river, based on the YOLOv5 algorithm and the coordinate transformation localization algorithm. The target recognition algorithm of this model introduces the attention mechanism into the backbone network and uses EIOU-Focal Loss as its loss function to improve YOLOv5 in detection accuracy and convergence speed. The results show that data enhancement improves the model’s target recognition and raises the mean Average Precision (mAP) by 9.9%. The ablation experiment results verify that this model has the highest detection accuracy, with its maximum mAP of 0.96 or an increase of 11.6% relative to the one before improvement. Its positioning results show the real average error of the algorithm target object is not greater than 3m, which means a high accuracy. Application of the improved model to the Minjiang River section in Fujian has verified its higher accuracy in detecting target objects and its significance for related research.

Concrete is a key basic material for the construction of hydraulic and hydropower projects in China. However, as a typical porous medium material, it is prone to ionic erosion, product corrosion, and matrix cracking in the environment of hydraulic engineering, leading to corroded reinforcement and reduced structural bearing capacity. At present, the existing concrete materials are difficult to satisfy the requirements by the design of long-life, high-quality projects in hydraulic and hydropower engineering in the western plateau or the southern coastal regions. This paper first summarizes the damage mechanism and challenges faced by traditional hydraulic concrete structural materials under severe environments, and then discusses in detail the long-life design method and performance enhancement mechanism of these concrete materials. Finally, we give an outlook on the future application and development of artificial intelligence in the long-life design and use of such materials, so as to provide new methods and new ideas for safe operation and maintenance and long-lasting service of the national major hydraulic and hydropower projects.