It is crucial for flood discharge structure vibration safety evaluations to filter low-frequency noise, separate dense-frequency components and obtain high-frequency component accurately from vibration signals. Variational mode decomposition, a novel signal adaptive decomposition method, effectively processes flood discharge structures. However, the mode number and quadratic penalty item uncertainty in variational mode decomposition directly affects the vibration signal decomposition. Therefore, an improved variational mode decomposition method for vibration signal processing is proposed in this study. The proposed method adaptively determines the mode number based on singular entropy and frequency stability to completely separate the structural vibration components (including dense-frequency components and high-frequency components) and noise components from the vibration signal. Next, an objective quadratic penalty item function based on sample entropy and mutual information is proposed to quantify the mode mixing between the structural vibration components. Finally, a particle swarm optimisation algorithm based on beetle antenna search is proposed to optimise the quadratic penalty item, which overcomes the shortcomings of traditional algorithms and suppresses the mode mixing between the structural vibration components. The validity and feasibility of the proposed method was verified by the simulation signal and was applied to a sluice prototype project. The results showed that the method effectively filtered noise, greatly improved the vibration response signal-to-noise ratio and obtained the structural vibration component time history signal, which provides a foundation for flood discharge structure vibration safety evaluation and health monitoring.

Complete pump characteristics (CPCs) are the key for establishing pump boundary conditions and simulating hydraulic transients. However, they are not normally available from manufacturers, making pump station design difficult to carry out. To solve this issue, a novel method considering the inherent operating characteristics of the centrifugal pump is therefore proposed to predict the CPCs. First, depending on the Euler equations and the velocity triangles at the pump impeller, a mathematical model describing the complete characteristics of a centrifugal pump is deduced. Then, based on multiple measured CPCs, the nonlinear functional relationship between the characteristic parameters of the characteristic operating points (COPs) and the specific speed is established. Finally, by combining the mathematical model with the nonlinear relationship, the CPCs for a given specific speed are successfully predicted. A case study shows that the predicted CPCs are basically consistent with the measured data, showing a high prediction accuracy. For a pump-failure water hammer, the simulated results using the predicted CPCs are close to that using the measured data with a small deviation. This method is easy to program and the prediction accuracy meets the requirements for hydraulic transient simulations, providing important data support for engineering design.

Surge tanks (STs) are important facilities for ensuring the safety of hydropower stations. Reducing the ST size under the premise of ensuring stable mass oscillations within the ST is the main issue. First, according to the basic equations of the mass oscillation for a hydropower station with an ST, a novel expression of the critical stability section of an ST is deduced considering the velocity head and throttle loss. Then, the sensitivity of each influencing factor of the proposed stability criterion is analyzed. Ultimately, through the simulation of small oscillation transients in two case studies, the water level oscillations (WLOs) in an ST based on three stability criteria are compared. The results show that a 20% smaller ST in a hydropower station may result in 10.4% larger oscillations and a 60% smaller ST in a pumped storage power station may result in 14.3% larger oscillations. Compared with the Thoma criterion and the Chinese specification criterion, the stability criterion proposed in this paper can safely reduce the size of the ST since it considers the influence of the velocity head and throttle loss. The proposed stability criterion can provide an important reference for the optimal design of the STs.

The large-scale integration of renewable energy, driven by the China’s carbon peaking and carbon neutrality goals, has become an inevitable trend in modern power systems, yet its inherent stochasticity and volatility challenge system stability and economic operation. This study takes a prefecture-level city in East China (thermal 42.1%, wind 23.6%, photovoltaic 34.3%) as a case and develops a capacity configuration optimization model for a thermal–wind–photovoltaic–pumped storage integrated system (TWPIS). Using wind, photovoltaic, and load data from 2019, the Synchronous Backward Reduction (SBR) algorithm generates 27 typical scenarios to characterize source–load uncertainty. A bi-level optimization framework is then established: the outer level optimizes renewable capacity using the Multi-Objective Multi-Verse Optimizer (MOMVO), while the inner level solves a mixed-integer linear programming (MILP) operational model via CPLEX combined with the ε-constraint method. The optimal configuration is selected from the Pareto frontier through an entropy-weighted TOPSIS approach. Results show that introducing pumped storage reduces the required wind and photovoltaic capacities from 3,978 MW and 2,274 MW to 2,700 MW and 1,110 MW, respectively. Through peak-shaving and valley-filling, wind and photovoltaic curtailment rates drop from over 20% and 50% to below 6%, while the total operating cost decreases by approximately 47% on average. Annual time-series simulation further confirms that the system operates efficiently and stably for more than 75% of the time. The findings demonstrate that pumped storage plays a critical role in enhancing flexibility, promoting renewable integration, and ensuring economic operation of high-renewable power systems.

Industrial power plant circulating cooling systems feature large flow rates, large pipe diameters, and severe hydraulic transients. To address excessive negative pressure and water hammer caused by flow interruption during pump shutdowns, a collaborative optimization method is proposed. It combines a LightGBM-TPE (Light Gradient Boosting Machine with Tree-structured Parzen Estimator) surrogate model with an improved multi-objective Grey Wolf algorithm (DEMOGWO). First, a hydraulic transient numerical model is constructed using the method of characteristics. Air valve size, inlet/outlet coefficients, and two-stage butterfly valve closing times are selected as decision variables. Using Latin hypercube sampling, a dataset is generated to train the LightGBM-TPE surrogate model for efficient water hammer simulation, while the SHAP method identifies core influencing parameters. Next, incorporating a nonlinear convergence factor, differential evolution mutation, and elite archiving, the DEMOGWO algorithm achieves dual-objective collaborative optimization of minimum system pressure (safety) and air valve size (economy). Results show the LightGBM-TPE model significantly outperforms CNN, BP, and LSTM networks in prediction accuracy. SHAP analysis reveals the butterfly valve's first-stage closing time and air valve size dominate negative pressure control. Post-optimization, the minimum system pressure improved from -3.98 m to -1.16 m (a 70.9% increase), with all transient values meeting safety limits. This "surrogate modeling-interpretability-optimization" framework provides an efficient solution for the intelligent tuning of water hammer protection parameters in power plants.