Search Results (90)

Aiming at the economic problems of industrial users with wind power, photovoltaic, and small hydropower resources in clean energy consumption and trading with superior power grids, this paper proposes a distributed pumped storage capacity optimization configuration method considering clean energy systems. First, considering the maximization of the investment benefit of distributed pumped storage as the upper goal, a configuration scheme of the installed capacity is formulated. Second, under the two-part electricity price mechanism, combined with the basin hydraulic coupling relationship model, the operation strategy optimization of distributed pumped storage power stations and small hydropower stations is carried out with the minimum operation cost of the clean energy system as the lower optimization objective. Finally, the bi-level optimization model is solved by combining the alternating direction multiplier method and CPLEX solver. This study demonstrates that distributed pumped storage implementation enhances seasonal operational performance, improving clean energy utilization while reducing industrial electricity costs. A post-implementation analysis revealed monthly operating cost reductions of 2.36, 1.72, and 2.13 million RMB for wet, dry, and normal periods, respectively. Coordinated dispatch strategies significantly decreased hydropower station water wastage by 82,000, 28,000, and 52,000 cubic meters during corresponding periods, confirming simultaneous economic and resource efficiency improvements. Full article

A high-power, narrow-linewidth, all-fiber polarization-maintaining (PM) amplifier has been demonstrated. A lasing power of 5870 W has been delivered in master oscillator power amplifier architecture with cascaded white noise source (WNS) phase modulation and bidirectional pumping schemes. The maximal power was limited by the onset of stimulated Brillouin scattering. At the maximum power operation, the amplifier exhibited a 3 dB spectral linewidth of 0.43 nm with beam quality being M² < 1.33 and polarization extinction ratio (PER) being 16.3 dB. To the best of our knowledge, this represents the highest spectral brightness and PER achieved by PM fiber laser systems around 6 kW-level operation. Full article

The construction of pumped storage power stations (PSPSs) is undergoing rapid expansion globally. Detecting operational faults and defects in pumped storage units is critical, as effective diagnostic methods can not only identify fault types quickly and accurately but also significantly reduce maintenance costs. This study leverages the symmetry characteristics in the vibration signals of pumped storage units to enhance fault diagnosis accuracy. To address the challenges of selecting the key parameters (e.g., decomposition level and penalty factor) of the variational mode decomposition (VMD) algorithm during vibration signal analysis, this paper proposes an algorithm for an improved subtraction-average-based optimizer (ISABO). By incorporating piecewise linear mapping, the ISABO enhances parameter initialization and, combined with a balanced pool method, mitigates the algorithm’s tendency to converge to local optima. This improvement enables more effective vibration signal denoising and feature extraction. Furthermore, to optimize hyperparameter selection in the bidirectional long short-term memory (BILSTM) network—such as the number of hidden layer units, maximum training epochs, and learning rate—we introduce an ISABO-BILSTM classification model. This approach ensures robust fault diagnosis by fine-tuning the neural network’s critical parameters. The proposed method is validated using vibration data from an operational PSPS. Experimental results demonstrate that the ISABO-BILSTM model achieves an overall fault recognition accuracy of 97.96%, with the following breakdown: normal operation: 96.29%, thrust block loosening: 98.60%, rotor-stator rubbing: 97.34%, and rotor misalignment: 99.59%. These results confirm that the proposed framework significantly improves fault identification accuracy, offering a novel and reliable approach for PSPS unit diagnostics. Full article

Pumped storage hydropower plants are important resources for scheduling urban energy storage, which realize the conversion of electric energy through upper and lower reservoir capacities. Dynamic forecasting of reservoir capacities is crucial for scheduling pumped storage and maximizing the economic benefits of pumped storage hydropower plants. In this work, a hybrid forecast network is proposed for both the upper and lower reservoir capacities of a pumped storage hydropower plant. A bidirectional long- and short-term memory network (BiLSTM) is designed as the baseline for the prediction model. A convolutional neural network (CNN) and Squeeze-and-Excitation (SE) attention mechanism are designed to extract local features from raw time series data to capture short-term dependencies. In order to better distinguish the effects of different data types on the reservoir capacity, the correlation between data and reservoir capacity is analyzed using the Spearman coefficient, and a multi-branch forecast model is established based on the correlation. A fusion module is designed to weight and fuse the branch prediction results to obtain the final reservoir capacities forecast model, namely, Multi-Branch Attention–CNN–BiLSTM. The experimental results show that the proposed model exhibits better forecast accuracy in forecasting the reservoir capacity compared with existing methods. Compared with BiLSTM, the $MAPE$ of the forecast values of the reservoir capacities of the upper and lower reservoirs decreased by $1.93\%$ and $2.2484\%$, the $RMSE$ decreased by $16.9887{\mathrm{m}}^3$ and $14.2903{\mathrm{m}}^3$, and the $R^2$ increased by $0.1278$ and $0.1276$, respectively. Full article

Tangerine peel, rich in moisture (75–90%) and medicinal value, requires drying to prevent spoilage and extend shelf life. Traditional heat pump drying often causes uneven airflow, leading to inconsistent drying and nutrient loss, compromising product quality and storage stability. In this study, a prediction model of drying moisture ratio of tangerine peel based on Kolmogorov–Arnold network bidirectional long short-term memory (KAN-BiLSTM) and multimodal feature fusion is proposed. A pre-trained visual geometry group U-shaped network (VGG-UNet) is employed to segment tangerine peel images and extract color, contour, and texture features, while airflow distribution is simulated using finite element analysis (FEA) to obtain spatial location information. These multimodal features are fused and input into a KAN-BiLSTM model, where the KAN layer enhances nonlinear feature representation and a multi-head attention (MHA) mechanism highlights critical temporal and spatial features to improve prediction accuracy. Experimental validation was conducted on a dataset comprising 432 tangerine peel samples collected across six drying batches over a 480 min period, with image acquisition and mass measurement performed every 20 min. The results showed that the pre-trained VGG-UNet achieved a mean intersection over union (MIoU) of 93.58%, outperforming the untrained model by 9.41%. Incorporating spatial features improved the coefficient of determination (R²) of the time series model by 0.08 ± 0.04. The proposed KAN-BiLSTM model achieved a mean absolute error (MAE) of 0.024 and R² of 0.9908, significantly surpassing baseline models such as BiLSTM (R² = 0.9049, MAE = 0.0476) and LSTM (R² = 0.8306, MAE = 0.0766), demonstrating superior performance in moisture ratio prediction. Full article

Background: Modern aquaculture is increasingly adopting industrialized recirculating aquaculture systems, in which the stable operation of its circulating water pump is essential. Yet, given the complex working conditions, this pump is prone to malfunctioning, so its timely fault prediction and accurate diagnosis are imperative. Traditional fault detection methods rely on manual feature extraction, limiting their ability to identify complex faults, and deep learning methods suffer from unstable recognition accuracy. To address these issues, a three-class fault detection method for water pumps based on a convolutional neural network, transformer, and bidirectional gated recurrent unit (CNN-transformer-BiGRU) is proposed here. Methods: It first uses the continuous wavelet transform to convert one-dimensional vibration signals into time–frequency images for input into a CNN to extract the time-domain and frequency-domain features. Next, the transformer enhances the model’s hierarchical learning ability. Finally, the BiGRU captures the forward/backward feature information in the signal sequence. Results: The experimental results show that this method’s accuracy in fault detection is 91.43%, significantly outperforming traditional machine learning models. Using it improved the accuracy, precision, and recall by 1.86%, 1.97%, and 1.86%, respectively, relative to the convolutional neural network and long short-term memory (CNN-LSTM) model. Conclusions: Hence, the proposed model has superior performance indicators. Applying it to aquaculture systems can effectively ensure their stable operation. Full article

To address the challenges in predicting roof water hazards in weakly cemented strata of Northwest China, this study pioneers an integrated CNN-BiLSTM model for aquifer water abundance prediction. Focusing on the 1301E working face in the Yili No. 1 Coal Mine, we employed kriging interpolation to process sparse hydrological datasets (mean relative error: 8.7%), identifying five dominant controlling factors—aquifer burial depth, hydraulic conductivity, core recovery rate, sandstone–mudstone interbedded layer count, and sandstone equivalent thickness. The proposed bidirectional architecture synergizes CNN-based spatial feature extraction with BiLSTM-driven nonlinear temporal modeling, optimized via Bayesian algorithms to determine hyperparameters (32-channel convolutional kernels and 64-unit BiLSTM hidden layers). This framework achieves the comprehensive characterization of multifactorial synergistic effects. The experimental results demonstrate: (1) that the test set root mean square error (1.57 × 10⁻³) shows 65.3% and 85.9% reductions compared to the GA-BP and standalone CNN models, respectively; (2) that the coefficient of determination (R² = 0.9966) significantly outperforms the conventional fuzzy analytic hierarchy process (FAHP, error: 0.071 L/(s·m)) and BP-based neural networks; (3) that water abundance zoning reveals predominantly weak water-rich zones (q = 0.05–0.1 L/(s·m)), with 93.3% spatial consistency between predictions and pumping test data. Full article

As the construction industry shifts from an extensive development model to one characterized by intelligent structural systems, the imperative to enhance productivity and management efficiency has emerged as a critical challenge. Conventional rebar construction processes heavily rely on manual operations—such as on-site rebar cutting, manual transcription of material lists, and decentralized processing—which are susceptible to subjective errors and often result in significant material waste. This issue is particularly pronounced in large-scale projects, where disorganized management of rebar quantities and placements exacerbates inefficiencies. To address these challenges, this study proposes an integrated approach that synergistically combines a genetic algorithm-based rebar-cutting optimization model with BIM technology, thereby optimizing rebar management throughout the construction process. The research is structured into two primary components. Firstly, a one-dimensional mathematical model for rebar-cutting optimization is developed, incorporating an innovative real-number encoding strategy within the genetic algorithm framework to maximize material utilization. A case study conducted on a pump station project reveals that the utilization rates for 32 mm and 16 mm rebar reach 86.76% and 93.90%, respectively, significantly exceeding the industry standard of 80%. Secondly, an automated batch modeling tool is developed using C# and the Revit API, which enables the efficient generation of rebar components; a unique coding system is employed to establish a bidirectional mapping between the digital model and the physical rebar, ensuring precise positioning and effective information management. Overall, this integrated method—encompassing rebar-cutting optimization, digital modeling, and on-site intelligent management—not only mitigates material waste and reduces production costs but also markedly enhances construction efficiency and accuracy in complex projects, thereby providing robust technical support for the seamless integration of intelligent construction and industrialized building practices. Full article

Aiming at the mechanical failures of vertical centrifugal pumps generated under the combined effects of centrifugal force and fluid power, which are difficult to be accurately recognized by traditional diagnostic methods, a vertical centrifugal pump fault diagnosis method based on the combination of convolutional neural network (CNN) and bidirectional long and short-term memory neural network (BiLSTM) is proposed. Firstly, a recurrent neural network is introduced on the basis of a convolutional neural network, and a feature extraction and fault classification module is established, which can automatically extract spatial and temporal features from the original input signals and identify the key fault modes. Subsequently, a vertical centrifugal pump fault simulation test rig is built, and the vibration signals of the centrifugal pumps under different faults are collected and used to train the proposed diagnostic model. Finally, the diagnostic models constructed by CNN and BiLSTM are compared with a single CNN diagnostic model for fault identification of vibration signals under different fault conditions. The results of the study show that the accuracy of fault diagnosis reaches 100% by using the technique of multi-channel information fusion, which verifies the advantages of multi-channel data fusion. Moreover, the addition of BiLSTM on the basis of CNN is able to better extract and capture useful information from the time series data. In summary, the method proposed in this study can effectively improve the fault diagnosis accuracy and reliability of vertical centrifugal pumps, providing a feasible technical solution for equipment health monitoring in engineering practice. Full article

In Germany, wind and photovoltaic (PV) systems dominate renewable electricity generation, with large wind turbines contributing 24.1% and PV systems contributing 10.6% in 2022. In contrast, electricity production from small wind turbines remains marginal (<0.01%). While currently only viable in high-wind locations, factors like rising electricity prices, cheaper battery storage, and increasing electrification could boost their future role. Within this work, a residential energy supply system consisting of a small wind turbine, PV system, heat pump, battery storage, and electric vehicle was dimensioned for different sites in Germany and Canada based on detailed simulation models and genetic algorithms in order to analyze the effect of bidirectional charging on optimal system dimensions and economic feasibility. This was carried out for various electricity pricing conditions. The results indicate that, with electricity purchase costs above 0.42 EUR/kWh, combined with a 25% reduction in small wind turbine and battery storage investment expenses, economic viability could be significantly enhanced. This might expand the applicability of small wind power to diverse sites. Full article

Digital twin (DT) technology has become a key enabler for prognostics and health management (PHM) in complex industrial systems, yet scaling predictive models for multi-component degradation (MCD) scenarios remains challenging, particularly when transferring insights from predictive models of smaller systems developed with limited data to larger systems. To address this, a physics-informed neural network (PINN) framework that integrates a standardized scaling methodology, enabling scalable DT analytics for MCD prognostics, was developed in this paper. Our approach employs a systematic DevOps workflow that features containerized PINN DT analytics deployed on a Kubernetes cluster for dynamic resource optimization, a real-time DT platform (PTC ThingWorx™), and a custom API for bidirectional data exchange that connects the cluster to the DT platform. A key contribution of this paper is the scalable DT model, which facilitates transfer learning of degradation patterns across heterogeneous hydraulic systems. Three (3) hydraulic system configurations were modeled, analyzing multi-component filter degradation under pump speeds of 700–900 RPM. Trained on limited data from a reference system, the scaled PINN model achieved 88.98% accuracy for initial degradation detection at 900 RPM—outperforming an unscaled baseline of 64.13%—with consistent improvements across various speeds and thresholds. This work advances PHM analytics by reducing costs and development time, providing a scalable framework for cross-system DT deployment. Full article

Photovoltaic energy is increasingly used in irrigation processes, particularly in arid regions, to pump water from rivers to fields. Rising oil prices, global warming, and the limited availability of fossil fuels have increased the need for alternative energy sources. This study focuses on the design and implementation of a transformerless single-phase photovoltaic system that powers a single-phase induction motor to drive a centrifugal water pump. The methodology aims to achieve the best system performance. A DC–DC boost converter maximizes the output voltage by utilizing maximum power point tracking (MPPT) and extracting the maximum power from the photovoltaic (PV) array. A bidirectional buck-boost converter charges the battery from the DC bus and discharges the battery voltage to the DC bus for loads. The DC voltage is then converted to AC output voltage using a single-phase inverter, which supplies power to the single-phase induction motor driver (IMD). The voltage/frequency (V/f) scaler control is used for a single-phase induction motor. The system employs scalar motor control to achieve the maximum motor speed required to operate the centrifugal water pump efficiently. All results and simulations are carried out in MATLAB/Simulink R2019a version and are compared for different motor and PV parameters numerically. Full article

Theoretical analysis, numerical simulation, and experimental study are used to investigate the ultra-low head bidirectional shaft extension pump, especially near-zero head conditions. The results show that under forward operation, at low flow and design flow conditions, the closer to the shroud, the closer the vortex is to the back of the guide vanes, and the vortex area is becoming smaller. The hydraulic loss of the outlet passage is 15% of the operating head at the minimum flow and 170% of the operating head under near-zero head condition. The peak-to-peak (PTP) value of pressure fluctuation increases with the increase in flow rate. The primary frequency (PF) of vibration is strongly related to the primary and secondary frequencies (PSFs) of pressure fluctuation. Under reverse operation, when the flow rate is less than 0.83Q_r0, the uniformity of axial velocity distribution V_u and the velocity-weighted average angle θ show an approximately exponential declining pattern. The hydraulic loss of the outlet passage at the minimum flow rate is 61% of the operating head and 350% of the operating head under near-zero head condition. The exponential fitting can better describe the relationship between circulation and hydraulic loss. As the flow rate decreases, the PF of vibration decreases to rotational frequency. Full article

Under the background of dual carbon, the retrofitting of the equipment operation system of a refrigeration station and the optimization combination of its control system are significant for its efficient operation and energy saving. The single-direction variable flow technology is often used in the chilled water system in refrigeration stations nowadays. However, the single-direction variable flow technology cannot achieve both thermal balance and flow balance for the chiller system, which is unfavorable for improving energy efficiency and reliability. To improve the reliability and energy efficiency of the refrigeration station equipment, the bidirectional variable flow technology of primary and secondary chilled water pumps was presented. Meanwhile, the feasibility of fuzzy neural networks in bidirectional variable flow systems and their energy-saving effect were studied. Before the energy saving retrofit, the refrigeration station used traditional PID (proportional-integral-derivative) controllers, and the chilled water system used single-direction variable flow technology; After the energy-saving retrofit, the refrigeration station adopted a fuzzy neural network control algorithm to optimize the PID controller parameters, and at the same time, the chilled water system used bidirectional variable flow technology. Through a large number of trial calculations of the established neural network model, it was found that 2 hidden layers and 25 hidden layer nodes can achieve higher accuracy. Specifically, the controller of the central refrigeration station consists of a training neural network and a predictive neural network working in parallel. The task of training neural networks is to learn the relationship between different input parameters and the whole energy consumption. Then it serves as the excitation function of the prediction network. The function of the predictive neural network is to find the control parameters that minimize energy consumption. The application results showed that before and after the retrofit annual power consumption and energy-saving effects were very Significant. After the energy-saving retrofit of the refrigeration station, the energy saving is 422,775 KWh every year, the energy-saving rate is 11.67%, and the annual saving cost is about 0.3382 million yuan. The results demonstrated that bidirectional variable flow technology and its control methods were feasible, reasonable, and worthy of promotion. Full article

This paper investigated the optimization of the hardness and oscillation mode of flexible hydrofoils using bidirectional fluid–structure interaction (FSI) to address the issue of insufficient guidance in engineering applications. A two-dimensional flexible symmetric hydrofoil model of NACA0012 with a chord length of 1 m was constructed for this research. The hydrodynamic characteristics of low-frequency flexible hydrofoils with varying hardness and oscillation modes were analyzed through numerical simulation. The results indicated that the flexible hydrofoil with a Shore hardness of D50 exhibited the most optimal hydrodynamic performance under low-frequency conditions across the five groups of hardness tests. Among the three commonly utilized oscillation modes, the inboard oscillation mode demonstrated the most favorable performance. The hydrodynamic performance of the flexible hydrofoil surpassed that of the rigid hydrofoil in both inward and outward oscillation motions; however, it was inferior in pure pitching motions. Comparative analysis of the vortex structure and velocity distribution in the flow field revealed that the inward oscillation motion effectively enhanced the kinetic energy of the wake vortex and slowed down vortex dissipation, thereby improving the overall flow velocity. These findings provide theoretical support for the study of flexible hydrofoils and contribute to their advancement in pumping applications under actual ultra-low head conditions. Full article