To improve the efficiency and accuracy of fault diagnosis for hydroelectric units, combination of multifractal detrended fluctuation analysis algorithm and probabilistic neural network was used to establish a vibration signal feature extraction and recognition model. The binary gravity search algorithm was used to optimize its parameters. The results show that the classification accuracy of the feature extraction and recognition classification model can be improved to 99% and reduce the signal processing time to about 1.3 seconds after optimizing by the binary gravity search algorithm. The proposed vibration signal feature extraction and recognition model for hydroelectric units can significantly distinguish between the normal working state and the fault working state of hydroelectric units, achieving the purpose of using vibration signal features to diagnose faults in hydroelectric units.

Cemented gravel dam construction technique combines the advantages of earth-rock dams and concrete dams, and has broad application prospects. Taking Shaping first-level Hydropower Station as an example, production tests were carried out on C₁₈₀6, C₁₈₀10 and C₁₈₀20 cemented sand and gravel, and core drilling tests were carried out on their compressive strength, splitting tensile strength, permeability and SEM scanning tests at the age of 180 days. The results show that under scientific ratio and reasonable construction technology, the compressive strengths of C₁₈₀6, C₁₈₀10 and C₁₈₀20 have reached 7.1 MPa, 14.8 MPa and 29.4 MPa, respectively. Among them, the impermeability grade of C₁₈₀10 with the largest amount of project consumption is W8. The performance of cemented sand and gravel can achieve the expected results, which verifies the feasibility of the construction plan. Thus, it provides technical support for subsequent construction, and also provides reference examples for subsequent hydropower station construction.

The Longyangxia and Liujiaxia Reservoirs in the upper reaches of the Yellow River have annual regulation capacity and undertake comprehensive utilization tasks such as flood control, water supply and irrigation, and power generation in the Yellow River Basin. The coordination and consistency of multiple objectives need to be achieved by constructing a multi-objective optimized dispatching system for cascade reservoirs. A multi-objective scheduling model has been established for the Longyangxia-Liujiashan cascaded reservoirs, with the goals of maximizing the peak shaving rate, total power generation, and average sediment flushing ratio. The model is solved using the NSGA-Ⅲ algorithm, and an analysis is conducted regarding the competitive relationships among the objectives of flood control, power generation, and sediment flushing. The established multi-objective optimization scheduling scheme is further evaluated through a developed indicator system, and the TOPSIS method is applied to optimize the set of scheduling solutions. The results show that there is a significant competitive relationship between the objectives of power generation and flood control; No significant competition exists between the objectives of sediment discharge and flood control, and there is some competition between the objectives of sediment discharge and power generation. Through a comparison of the optimal scheme and the actual scheduling data, it can be seen that the benefits of flood control, power generation, and sediment discharge in the optimal scheme increased by 20.89%, 16.02%, and 3.61%, respectively, compared to the actual scheduling.

Constructing a high-precision dam settlement prediction model is of great significance for ensuring the safety and risk control of dam during the construction period. Taking dam height, rainfall and aging as the influencing factors of dam settlement deformation during construction period, the long-term and short-term memory neural network LSTM algorithm is introduced, and the attention mechanism is embedded. Thus, a prediction model suitable for dam settlement of concrete face rockfill dam during construction period is proposed. The engineering application shows that the attention-LSTM model makes up for the defect that the LSTM cannot dynamically adjust the weight coefficient at the network layer, improves the computational efficiency and accuracy of the model, and has better nonlinear data processing ability, which can more accurately reflect the change trend of monitoring data in the time dimension during the construction period. The relevant experience can be used as a reference for similar projects.

The maximum water head endured by the floor of the high-pressure branch pipe at the Zhongdong Pumped Storage Power Station in Huizhou, Guangdong, is approximately 800 m during operation. The stability of the surrounding rock under this high internal water pressure is critical to the station's safe operation. To address this, in-situ stress and high-pressure water injection tests were conducted. Combined with three-dimensional in-situ stress field inversion, the stress field distribution, permeability characteristics, and hydraulic fracturing resistance of the high-pressure branch pipe area were analyzed, and the layout of bifurcated pipe was optimized. The results indicate that the maximum principal stress in the high-pressure branch pipe section ranges from 15.0 to 16.6 MPa, and the minimum principal stress ranges from 8.4 to 9.7 MPa. The rock permeability ranges from 0.01 to 0.19 Lu, indicating very low to low permeability. The initial high-pressure branch pipe location meets the stability requirements against uplift and seepage. However, within a 7 m range of the branch pipe opening, the class Ⅲ rock mass segment is affected by faults and does not meet the engineering requirements for hydraulic fracturing resistance. Based on a comprehensive analysis of the surrounding rock conditions, uplift resistance, hydraulic fracturing resistance, and seepage resistance, the initial high-pressure branch pipe location was shifted 10 m toward the powerhouse, which meets the stability requirements for uplift, hydraulic fracturing resistance, and seepage resistance.

There are problems of non-convergence and easy false alarm when formulating dam deformation monitoring indicators (which belong to fixed limits) based on the conventional low-probability method. A calculation method for formulating deformation monitoring indicators based on the low-probability method of separating aging components is proposed. Firstly, the statistical model of dam deformation is established to separate the time-dependent component. Then, aiming at the time series deformation of deducting the aging component, the annual extreme value is selected as the subsample. The corresponding deformation of the annual most unfavorable reservoir water level and temperature is selected as the subsample. The corresponding deformation of the unfavorable water level and temperature based on the combination of orthogonal test method is selected as the subsample. Then the statistical test is carried out, and the small probability method is used to formulate the deformation allowable value of deducting the aging component. Finally, the aging component is superimposed to obtain the non-convergence deformation monitoring index of the dam. Combined with the measured data of a deformation non-convergence gravity dam in southwest China, the analysis shows that compared with the monitoring index proposed by the conventional small probability method, the method based on the separation time component fully considers the time effect and enhances the reliability of the monitoring index.

Hydroelectric units generally use Babbitt Alloy with strong friction reduction properties as the bearing bush material. However, due to the bearing bush made by Babbitt Alloy material has lower strength, it can not be used in the need to withstand the larger pressure of large-scale hydropower generating sets. The Steel Kogu (SK) can withstand large loads while having good wear resistance. Therefore, the oil film temperature, bearing bush body temperature and power of SK bearing pad and Babbitt Metal bearing bush are studied comparatively through experiments. The thickness, pressure, maximum temperature of oil film, base deformation and power loss of the two types of bearing bush are studied comparatively by numerical calculation. The results show that at the same oil inlet temperature, the base deformation and maximum oil film temperature of SK bearing bush are lower than that of Babbitt Alloy by 0.06-0.08 mm and 9.6-14.6 ℃, respectively, and the oil film thickness of SK bearing bush is higher than that of Babbitt Alloy by 13-15 μm. The SK bearing bush can be used as a potential choice of bearing bush material for large-scale hydroelectric units because of its lower oil film temperature and smaller base deformation during the operating process.

In order to explore the influence of pressure pulsation of the pumping unit on the powerhouse structure, the powerhouse of Dayuzhang pumping station was taken for an example. Based on the prototype observation data, the vibration source composition and vibration characteristics of powerhouse structure were analyzed using three-dimensional finite element simulation. The safety of the structure was analyzed and evaluated from the perspective of structural resonance check and vibration response. The results show that the hydraulic pulsation caused by RSI and the rotational frequency excitation caused by the operation of the unit have the greatest impact on the vibration of powerhouse under the stable operation condition of the unit, and the natural frequency of the local floor structure has a small degree of coincidence, which is easy to resonate. However, from the perspective of vibration response, the vibration response of each local part is within the allowable range, the outlet elbow and pump seat are the largest, and the pump floor slab is the smallest. This study has important theoretical value and practical significance for realizing the long-term and safe operation of the pumping station powerhouse structure.

With the construction and operation of integrated clean energy bases, there is an urgent need for multi-energy joint dispatch. On the basis of cascade hydropower joint scheduling, this article embeds the risk of channel electricity curtailment as a penalty constraint, integrates the working experience of scheduling personnels with rolling ideas, and explores a method for formulating multi-time scale cascade hydro-photovoltaic complementary joint scheduling rules based on actual operation. The potential risks of power abandonment is identified in advance and control measures are proposed. By selecting the benefits of hydroelectric power generation and energy storage, as well as photovoltaic power generation, a complementary function of hydro-photovoltaic joint system is established to evaluate the scheduling rules. The proposed method has been applied to the hydro-photovoltaic complementary system of Xiaowan and Manwan on the Lancang River. The results show that the economic benefits of the hydro-photovoltaic joint system is significantly increased without significantly affecting the hydropower regulations. The idea has the feasibility of promoting the joint operation of the integrated hydro -photovoltaic energy storage and clean energy watershed base in future.

Affected by hydrodynamic excitation and other factors, the opening-closing operation of hydraulic gates exhibits multi-field coupling effects and complex nonlinear dynamic characteristics, leading to difficulties in identifying equipment safety states. Test data of gate operation demonstrate that artificial neural network algorithms can identify hydrodynamic excitation disease features and accurately predict its development trends. To address this, BP and GA-BP neural networks were employed to construct identification and prediction models for hydrodynamic excitation disease. These models were applied to identify and forecast the effective values of reel vibration, with model performance evaluated using metrics including Relative Error (R_RE), Mean Absolute Percentage Error (M_MAPE), and Root Mean Square Error (R_RMSE). Compared to the BP model, the results indicate that the GA-BP model achieves reductions of 20.77% in R_RE, 4.74% in M_MAPE, and 6.27% in R_RMSE, demonstrating superior fitting to measured samples and enhanced stability with extended prediction durations, thus providing critical technical support for engineering risk mitigation and hazard prevention.