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 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.

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.

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.

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.

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.

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.

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.

In water conservancy and hydropower projects, construction safety hazards come large in volume and are usually diversified in forms and types. The same hidden danger may involve multiple types, but the definition of its types is ambiguous; previous classification of hazard types is mostly dominated by manual experience, easily leading to confusion and difficulty in hazard management. To address such issues, this paper presents a multi-label intelligent identification method for construction safety hazards in hydropower projects. First, the ALBERT model is used to encode text information to achieve a high-precision quantification of the unstructured risk texts. Then, we construct a multi-label intelligent classification model of safety hazards text to improve identification efficiency, considering the content weight of the Chinese text for safety hazards and using the Bidirectional Gated Recurrent Unit (Bi-GRU) improved by the Attention mechanism. Finally, the performance of this method is tested using the texts of hydropower engineering construction safety hazards, verifying its F1 value reaches 92.11% compared with previous text classification methods. It is proved applicable, as a useful support to safety management of hydropower construction and analysis of its safety hazards.

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.

The poor water mobility of river channels in a plain river network region is the main cause of severe water environment pollution; water diversion is an important measure to improve the water environment in such regions. This paper constructs a hydrodynamic and water quality coupling model for river networks based on the measured data, and presents an application to the Sanshanwei area in Foshan, Guangdong. This model is verified against a 15-day continuous series of hydrodynamic and water quality monitoring data, and it is evaluated using the NSE and RMSE indexes. For this area, we have designed four gate control modes and seven landscape control water levels based on its terrain features, tidal motions, and landscape requirements. A total of 28 simulation conditions are used to simulate the river networks and analyze the mechanism of its all-sided responses to different conditions－including the improvement of its hydrodynamic water quality and the variations in the tidal levels of its inner and outer rivers; water diversion flow and drainage flow, and their spatial distributions; gate control modes and landscape water levels. The results show the model is reasonable and reliable. The study area is affected by its drainage path and has a significant difference in river flow mobility. We have achieved very significant improvement of the water environment through considering comprehensively the effects of river flow distribution, water flow path, and diversion effect of bifurcated river channels; considering the pollutant concentration in the outer rivers on the water quality; combining effectively the dynamic water environment capacity of the tidal river network and the spatial and temporal distribution of pollution source discharge. Compared with the low landscape control water level, our control modes of high landscape water levels can increase the dynamic water environment capacity of inner rivers, resulting in relatively low pollutant concentration. When the control level is raised from 0.2 to 0.8 m, the diversion flows in different diversion paths are increased by 28.0% - 64.7%, and the ammonia nitrogen concentration in the section reduced by 0.85 - 5.50 mg/L or a reduction ratio of 28.9% - 67.2%. This study presents a new idea for scheduling optimization and water environment in plain tidal river networks, useful for designing corresponding engineering measures.

The Tibetan Plateau, known as the "Asian Water Tower," is essential in terms of hydrology, climate, and ecology. At the moment, our understanding of the hydrological functions of the water tower is mostly focused on its storage function, with little attention paid to its regulation function and mechanism. In terms of watershed preservation and regulation, this study describes the hydrological functions of the water tower’s water storage and regulation, and the hydrological functions and storage-regulation mechanism of multi-phase water towers. The Tibetan Plateau Water Tower not just has a powerful function of water storage, but plays other roles－redistributing precipitation over the water tower region; adjusting the runoff and its distribution within a year in the non-water tower regions, and regulating water resources such as precipitation, surface water, soil water, and groundwater in the basin. This study helps lay a basis for assessing the ecological and hydrological impact of the water tower on the upper and lower basins of the plateau, useful for protection and regulation of the water tower on the basins, so as to meet the production, living, and ecological water needs of this region and its surroundings through rational planning of water conservancy projects.

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.

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 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.

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.

Detection of compaction quality during rockfill dam construction is an important safeguard for its safe and stable operation, but the traditional irrigation method relies on measuring the detection pit volume that is time-consuming and labor-intensive. Today, the three-dimensional laser scanning technology can be adopted to calculate the detection pit volume quickly and improve the efficiency of compaction quality detection. To ensure the calculation accuracy of this new method, the key link is accurate extraction of the detection pit point cloud data from the scanned data. This paper develops a point cloud extraction method based on point cloud slicing that is able to accurately extract the point cloud on the surface of the inspection pit. First, the point cloud data is sliced; then a one-way search sorting method is used to sort the points of each sliced point set; finally, a cluster segmentation method based on the sorting number of the sliced point set is adopted to segment each slice. We have designed a MATLAB code to realize rapid extraction of the point cloud for a rockfill dam inspection pit based on this new method, and tested it using the point cloud data to verify its effectiveness and accuracy.

During the construction of a hydropower project, a large number of non-editable documents for hydraulic concrete materials are generated. Using manual interpretation methods to obtain texts is time-consuming, laborious and accuracy-uncontrollable, making it difficult to meet the demand for information management of material data. This paper develops an intelligent interpretation method for non-editable texts of hydraulic concrete materials. First, we construct a text detection model, HC-PSENet, based on pixel level segmentation, which integrates the backbone network of PP-HGNet to achieve accurate detection of text lines. Then, a professional corpus is created based on the domain knowledge to realize accurate character mapping. We construct a text recognition model HC-CRNN for hydraulic concrete materials, using detection text boxes and the professional corpus as its inputs, and adopt the backbone network of ResNet and the improved loss function C-CTC Loss to improve the accuracy of character classification. Finally, a transfer learning strategy is adopted to train the model with the self-designed dataset as an example; the effectiveness and superiority of our new method is verified through ablation and comparative experiments. The results show that it has a harmonic mean of 0.985 for detecting text regions and its accuracy of text recognition reaches 90.62%. It has an overall performance superior to classical methods and would provide new technical means for the automated reuse of non-editable text resources in concrete materials.

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.

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.