Search Results (285)

After primary frequency regulation in large-scale wind farms is completed, the power dip phenomenon occurs during the rotor speed recovery phase. This phenomenon may induce a secondary frequency drop in power systems, which poses challenges to system frequency security. To address this issue, this paper proposes a frequency security-oriented optimal dispatch model for multi-regional power systems, taking into account the risks of secondary frequency drop. In the first stage, risk-averse day-ahead scheduling is conducted. It co-optimizes operational costs and risks under wind power uncertainty through stochastic programming. In the second stage, frequency security verification is carried out. The proposed dispatch scheme is validated against multi-regional frequency dynamic constraints under extreme wind scenarios. These two stages work in tandem to comprehensively address the frequency security issues related to wind power integration. The model innovatively decomposes system reserve power into three distinct components: wind fluctuation reserve, power dip reserve, and contingency reserve. This decomposition enables coordinated optimization between absorbing power oscillations during wind turbine speed recovery and satisfies multi-regional grid frequency security constraints. The column and constraint generation algorithm is employed to solve this two-stage optimization problem. Case studies demonstrate that the proposed model effectively mitigates frequency security risks caused by wind turbines’ operational state transitions after primary frequency regulation, while maintaining economic efficiency. The methodology provides theoretical support for the secure integration of high-penetration renewable energy in modern multi-regional power systems. Full article

With the rapid development of the Internet, the speed and scope of sudden public events disseminating in cyberspace have grown significantly. Current methods of quantifying public opinion heat often neglect emotion-driven factors and user interaction behaviors, making it difficult to accurately capture fluctuations during dissemination. To address these issues, first, this study addressed the complexity of interaction behaviors by introducing an approach that employs the information gain ratio as a weighting indicator to measure the “interaction heat” contributed by different interaction attributes during event evolution. Second, this study built on SnowNLP and expanded textual features to conduct in-depth sentiment mining of large-scale opinion texts, defining the variance of netizens’ emotional tendencies as an indicator of emotional fluctuations, thereby capturing “emotional heat”. We then integrated interactive behavior and emotional conflict assessment to achieve comprehensive heat index to quantification and dynamic evolution analysis of online public opinion heat. Subsequently, we used Hodrick–Prescott filter to separate long-term trends and short-term fluctuations, extract six key quantitative features (number of peaks, time of first peak, maximum amplitude, decay time, peak emotional conflict, and overall duration), and applied K-means clustering algorithm (K-means) to classify events into three propagation patterns, which are extreme burst, normal burst, and long-tail. Finally, this study conducted ablation experiments on critical external intervention nodes to quantify the distinct contribution of each intervention to the propagation trend by observing changes in the model’s goodness-of-fit ($R^2$) after removing different interventions. Through an empirical analysis of six representative public opinion events from 2024, this study verified the effectiveness of the proposed framework and uncovered critical characteristics of opinion dissemination, including explosiveness versus persistence, multi-round dissemination with recurring emotional fluctuations, and the interplay of multiple driving factors. Full article

The radial magnetic bearing system is an open-loop, unstable, strong nonlinear system with a high rotor speed, predisposition to jitter, and poor interference immunity. The system is subjected to the main interference generated by gravity, and rotor imbalance and sensor runout seriously affect the system’s rotor position control performance. A plug-in repetitive control method based on equivalent-input-disturbance (EID) is presented to address the issue of decreased control accuracy of the magnetic bearing system caused by disturbances from gravity, rotor imbalance, and sensor runout. First, a linearized model of the magnetic bearing rotor containing parameter fluctuations due to the eddy current effect and temperature rise effect is established, and a plug-in repetitive controller (PRC) is designed to enhance the rejection effect of periodic disturbances. Next, an EID system is introduced, and a Luenberger observer is used to estimate the state variables and disturbances of the system. The estimates of the EID are then used for feedforward compensation to address the issue of large overshoot in the system. Finally, simulations are conducted for comparison with the PID control method and PRC control method. The plug-in repetitive controller method assessed in this paper improves control performance by an average of 87.9% and 57.7% and reduces the amount of over-shooting by an average of 66.5% under various classes of disturbances, which proves the efficiency of the control method combining a plug-in repetitive controller with the EID theory. Full article

For cold venting processes frequently employed in oil and gas fields, precisely predicting the instantaneous diffusion process of the vented explosive and/or toxic gases is of great importance, which cannot be captured by the Reynolds-averaged Navier–Stokes (RANS) method. In this paper, the large eddy simulation (LES) method is introduced for gas diffusion in an open space, and the diffusion characteristics of the sulfur-containing natural gas in the cold venting process is analyzed numerically. Firstly, a LES solution procedure of compressible gas diffusion is proposed based on the ANSYS Fluent 2022, and the numerical solution is verified using benchmark experiments. Subsequently, a computational model of the sulfur-containing natural gas diffusion process under the influence of a wind field is established, and the effects of wind speed, sulfur content, the venting rate and a downstream obstacle on the natural gas diffusion process are analyzed in detail. The results show that the proposed LES with the DSM sub-grid model is able to capture the transient diffusion process of heavy and light gases released in turbulent wind flow; the ratio between the venting rate and wind speed has a decisive influence on the gas diffusion process: a large venting rate increases the vertical diffusion distance and makes the gas cloud fluctuate more, while a large wind speed decreases the vertical width and stabilizes the gas cloud; for an obstacle located closely downstream, the venting pipe makes the vented gas gather on the windward side and move toward the ground, increasing the risk of ignition and poisoning near the ground. The LES solution procedure provides a more powerful tool for simulating the cold venting process of natural gas, and the results obtained could provide a theoretical basis for the safety evaluation and process optimization of sulfur-containing natural gas venting. Full article

Accurate traffic forecasting is challenging due to the complex spatial-temporal interdependencies of large road networks and sudden speed changes caused by unexpected events. Traditional models often struggle with the non-stationary and volatile characteristics of traffic time series. While existing sequence decomposition methods can capture stable long-term trends and periodic information, they fail to address complex fluctuation patterns. To tackle this issue, we propose the Spatial-Temporal traffic flow prediction with residual and trend Decomposition Transformer (STDformer), which decomposes time series into different components, thus enabling more accurate modeling of both short-term and long-term dependencies. Our method processes the time series in parallel using the Trend Decomposition Block and the Spatial-Temporal Relation Attention. The Spatial-Temporal Relation Attention captures dynamic spatial correlations across the road network, while the Trend Decomposition Block decomposes the series into trend, seasonal, and residual components. Each component is then independently modeled by the Temporal Modeling Block to capture its unique temporal dynamics. Finally, the outputs from the Temporal Modeling Block are fused through a selective gating mechanism, combined with the Spatial-Temporal Relation Attention output to produce the final prediction. Extensive experiments on PEMS traffic datasets demonstrate that STDformer consistently outperforms state-of-the-art traffic flow prediction methods, particularly under volatile conditions. These results validate STDformer’s practical utility in real-world traffic management, highlighting its potential to assist traffic managers in making informed decisions and optimizing traffic efficiency. Full article

In oilfield operations, produced fluids consist of complex mixtures including heavy oil, sand, and water. Variations in sand particle parameters and operational conditions can significantly impact the performance of multiphase pumps. To elucidate the movement patterns of sand particles within a vane-type multiphase pump, this study employs the Discrete Phase Model (DPM) to investigate the effects of different sand particle parameters and operational conditions on the internal flow characteristics. The study found that: sand particle diameter, flow rate, rotational speed, and oil content significantly influence the trajectories of the solid–liquid two-phase flow, the motion characteristics of sand particles, and the vortices in the liquid flow field. As sand particle diameter increases, their radial and axial momentum first rise and then decline. Both radial and axial momentum are positively correlated with sand concentration. An increase in flow rate, higher rotational speed, and lower oil content all lead to greater fluctuations in the radial momentum curve of sand particles inside the impeller. Larger sand particles are predominantly distributed near the inlet, while smaller particles are more concentrated at the outlet. Higher sand concentrations and non-spherical particles increase particle distribution within the flow passages, with the guide vane channels exhibiting the most pronounced accumulation—reaching a maximum concentration of 6260 kg/m³ due to elevated sand loading. Increasing flow rate, rotational speed, or oil content significantly reduces sand concentration in the flow channel, promoting more efficient particle transport. Conversely, lower inlet sand concentration, non-spherical particles, reduced flow rate, decreased rotational speed, and higher oil content all result in fewer large particles in the flow passage. The findings provide important guidance for improving the wear resistance of vane-type multiphase pumps. Full article

Wind-induced vibrations in additional conductors on electrified railway catenary systems pose a risk to operational safety and long-term structural performance. This study investigates the dynamic response of these components under wind excitation through nonlinear finite element analysis. A wind speed spectrum model is developed using wind tunnel tests and field data, and the autoregressive method is used to generate realistic wind fields incorporating longitudinal, lateral, and vertical components. A detailed finite element model of the additional conductors and fittings was constructed using the Absolute Nodal Coordinate Formulation to account for large deformations. Time domain simulations with the Newmark-β method were conducted to analyze vibration responses. The results show that increased wind speeds lead to greater vibration amplitudes, and the stochastic nature of wind histories significantly affects vibration modes. Higher conductor tension effectively reduces vibrations, while longer spans increase flexibility and susceptibility to oscillation. The type of fitting also influences system stability; support-type fittings demonstrate lower stress fluctuations, reducing the likelihood of resonance. This study enhances understanding of wind-induced responses in additional conductor systems and informs strategies for vibration mitigation in high-speed railway infrastructure. Full article

Accurate solar photovoltaic (PV) capacity estimation requires high-resolution, site-specific solar irradiance data to account for localized variability. However, global datasets, such as the National Solar Radiation Database (NSRDB), provide regional averages that fail to capture the fine-scale fluctuations critical for large-scale grid integration. This limitation is particularly relevant in the context of increasing distributed energy resources (DERs) penetration, such as rooftop PV. Additionally, it is critical to the implementation of the U.S. Federal Energy Regulatory Commission (FERC) Order 2222, which facilitates DER participation in U.S. bulk power markets. To address this challenge, this study evaluates Nearest-Neighbor Random Forest (NNRF) and Nearest-Neighbor Gaussian Process (NNGP) models for spatiotemporal downscaling of global solar irradiance data. By leveraging historical irradiance and meteorological data, these models incorporate spatial, temporal, and feature-based correlations to enhance local irradiance predictions. The NNRF model, a machine-learning approach, prioritizes computational efficiency and predictive accuracy, while the NNGP model offers a level of interpretability and prediction uncertainty by numerically quantifying correlations and dependencies in the data. Model validation was conducted using day-ahead predictions. The results showed that the average Goodness of Fit (GoF) of the NNRF model of 90.61% across all eight sites outperformed the GoF of the NNGP of 85.88%. Additionally, the computational speed of NNRF was 2.5 times faster than the NNGP. Finally, the NNGP displayed polynomial scaling while the NNRF scaled linearly with increasing number of nearest neighbors. Additional validation of the model on five sites in Puerto Rico further confirmed the superiority of the NNRF model over the NNGP model. These findings highlight the robustness and computational efficiency of NNRF for large-scale solar irradiance downscaling, making it a strong candidate for improving PV capacity estimation and real-time electricity market integration for DERs. Full article

Analyzing the performance characteristics and applicable environments of intelligent vehicle lateral control algorithms, this paper establishes the Model Predictive Control (MPC), Pure Pursuit (PP), and Linear Quadratic Regulator (LQR) algorithms and uses 2022b MATLAB software to simulate the lateral error, heading error, and algorithm execution time at speeds of 3 m/s, 7 m/s, and 10 m/s. Urban low-speed scenarios (3 m/s) require high-precision control (such as obstacle avoidance), while high-speed scenarios (10 m/s) require strong stability. Existing research mostly focuses on a single speed and lacks a quantitative comparison across multiple operating conditions. Although MPC has high accuracy, its time consumption fluctuates greatly. LQR has strong real-time performance but a wide range of heading errors. PP has poor low-speed performance but controllable high-speed time consumption growth. It is necessary to define the applicable scenarios of each algorithm through quantitative data. In response to the lack of multi-speed domain quantitative comparison in existing research, this paper conducts multi-condition simulations using MPC, PP, and LQR algorithms and finds that at a low speed of 3 m/s, the peak lateral error of PP (0.45 m) is 55% and 156% higher than MPC (0.29 m) and LQR (0.176 m), respectively. At a speed of 10 m/s, the lateral error standard deviation of MPC (0.08 m) is reduced by 68% compared to PP (0.25 m). In terms of algorithm time consumption, LQR maintains full-speed domain stability (0.11–0.44 ms), while PP time increases by 95% with speed from 3 m/s to 10 m/s. The results show that in terms of lateral error, the MPC and LQR algorithms perform more stably, while the PP algorithm has a larger error at low speeds. Regarding heading error, all algorithms have a relatively large error range, but the MPC and LQR algorithms perform slightly better than the PP algorithm at high speeds. In terms of algorithm execution time, the LQR algorithm has the shortest and most stable execution time, the MPC algorithm has a relatively longer execution time, and the PP algorithm’s execution time varies at different speeds. Through this simulation, if high control accuracy and stability are pursued, the MPC or LQR algorithm can be considered; if real-time performance and computational efficiency are more important, the PP algorithm can be considered. Full article

With the increasing penetration of high-proportion renewable energy sources and large-scale integration of power electronic devices, distribution networks are evolving towards power-electronized systems. The integration of high-proportion renewable energy introduces challenges such as bidirectional power flow and voltage violations. Unlike traditional voltage regulation devices with slow and discrete adjustment characteristics, power electronic devices can continuously and rapidly respond to voltage fluctuations in distribution networks. However, the integration of power electronic devices alters the operational paradigm of distribution networks, necessitating adaptive voltage-reactive power control methods tailored to the regulation characteristics of both power electronic devices and discrete equipment. To fully exploit the real-time regulation capabilities of power electronic devices, this paper established a hierarchical coordinated control model for power-electronized distribution networks to achieve optimal voltage-reactive power control. A three-stage hierarchical coordinated control architecture is proposed based on the distinct response speeds of different devices. A variable-slope linear droop control method based on voltage boundary parameter optimization is employed for real-time adjustment of soft open point (SOP) and inverter outputs. To address uncertainties in PV generation and load demand, a rolling optimization strategy is implemented for centralized control, supplemented by probabilistic modeling to generate multiple representative scenarios for hierarchical coordinated control. Case studies demonstrate optimized operational results across centralized and local control stages, with comparative analyses against existing voltage-reactive power control methods confirming the superiority of the proposed hierarchical coordinated control framework. Full article

The marine environment is highly complex, characterized by substantial fluctuations in flow velocity. To enhance the adaptability of robotic large yellow croakers to such conditions, this study takes into account multiple factors, including shape, dimensions, and material properties, and evaluates their hydrodynamic resistance characteristics. A 2D model of large yellow croakers aged 1, 4, 7, 10, and 12 months was established as the bionic object. Based on computational fluid dynamics, the water resistance characteristics of this model were investigated in the same water environment. A 3D model of this species based on the 2D model and three skin materials, PE, PC, and ST, was added, and the effects of these materials on the water resistance of the 3D model were investigated. It was shown that in a water environment with a current speed of 0.1~1 m/s, the water resistance of large yellow croaker models at different ages ranged from 0.1006 to 6.8485 N; that of croakers with different body lengths ranged from 0.1067 to 28.5760 N; and that of croakers with different skin materials ranged from 0.0048 to 0.8672 N. The results showed that in the water environment with a current speed of 0.1–1 m/s, the 12-month-old large yellow croaker model had a lower water resistance range of 0.1006~3.6512 N in the watershed compared with other models of the same age; the large yellow croaker models with body lengths of 20, 30, and 40 cm had a smaller range of water resistance of 0.1125~12.5110 N in the watershed compared with other models of the same body length; and large yellow croaker models made of PE had a smaller range of resistance of 0.0048~0.7523 N in the watershed compared to those made of PC and ST materials. The results of this study are important for the design and fabrication of robotic fish capable of prolonged underwater operations. Full article

The motor drive system is pivotal for vehicles, particularly in new energy applications. However, conventional hybrid systems, which combine generator sets and single batteries in parallel configurations, fail to meet the operational demands of large pure electric mining dump trucks under fluctuating power requirements—such as high reserve power during acceleration and robust energy recovery during braking. Traditional single-motor configurations struggle to balance low-speed, high-torque operations and high-speed driving within cost-effective ranges, often necessitating oversized motors or multi-gear transmissions. To address these challenges, this paper proposes a dual-motor symmetric powertrain configuration with a seven-speed gearbox, tailored to the extreme operating conditions of mining environments. By integrating a high-speed, low-torque motor and a low-speed, high-torque motor through dynamic power coupling, the system optimizes energy utilization while ensuring sufficient driving force. The simulation results under extreme conditions (e.g., 33% gradient climbs and heavy-load downhill braking) demonstrate that the proposed configuration achieves a peak torque of 267 kNm (200% improvement over single-motor systems) and a system efficiency of 92.4% (vs. 41.7% for diesel counterparts). Additionally, energy recovery efficiency reaches 85%, reducing energy consumption to 4.75 kWh/km (83% lower than diesel trucks) and life cycle costs by 38% (USD 5.34/km). Field tests in open-pit mines validate the reliability of the design, with less than a 1.5% deviation in simulated versus actual performance. The modular architecture supports scalability for 60–400-ton mining trucks, offering a replicable solution for zero-emission mining operations in high-altitude regions, such as Tibet’s lithium mines, and advancing global efforts toward carbon neutrality. Full article

The removal of rust from large equipment such as trains and ship hulls poses a significant challenge. Traditional methods, such as chemical cleaning, flame rust removal, and laser rust removal, suffer from drawbacks such as high energy consumption, operational complexity, and poor mobility. Sandblasting and high-pressure water jet rust removal face issues such as high consumable costs and environmental pollution. Existing robotic grinding systems often rely on precise measurement of the workpiece surface geometry to perform deburring and polishing tasks; however, they lack the sufficient adaptability and robustness required for rust removal operations. To address these limitations, this study proposes a floating grinding actuator scheme based on compound force-position fuzzy control. By implementing simplified path-point planning, continuous grinding and rust removal can be achieved without requiring the pre-measurement of workpiece geometry data. This solution integrates force and laser displacement sensors to provide real-time compensation for path deviations and ensures adaptability to complex surfaces. A fuzzy derivative-leading PID algorithm was employed to control the grinding force, enabling adaptive force regulation and enhancing the control precision. Rust removal test results demonstrate that under varying advancing speeds, fuzzy derivative-leading PID control can significantly reduce fluctuations in both the grinding force and average error compared to traditional PID control. At a speed of 40 mm/s, excellent control performance was maintained, achieving a rust removal rate of 99.73%. This solution provides an efficient, environmentally friendly, and high-precision automated approach to rust removal using large-scale equipment. Full article

Large curvature, high pre-swirl large high-speed centrifugal fans are the preferred choice for industrial gas quenching furnaces, as they need to operate under non-uniform inlet conditions for extended periods. The resulting inlet distortion disrupts the symmetric flow of the gas, leading to reduced fan stability and phenomena such as flow separation and rotational stall. This issue has become a key research focus in the field of large centrifugal fan applications. This paper introduces an eddy viscosity correction method, and compares it with experimental results from U-shaped pipe curved flow. The corrected SST k-ω model shows a maximum error of only 4.7%. Simulation results show that the fan inlet generates a positive pre-swirl inflow with a relative distortion intensity of 3.83°. The flow characteristics within the impeller passage are significantly affected by the swirl angle distribution. At the maximum swirl angle, the leakage flow at the blade tip develops into a stall vortex that spans the entire passage, with an average blockage coefficient of 0.29. At the minimum swirl angle, the downstream leakage flow at the blade tip is suppressed on the suction side by the main flow, leading to a reduced vortex structure within the passage and an average blockage coefficient of 0.21. To address the design challenges of large high-speed centrifugal fans under inlet distortion, a blade design method based on secondary flow suppression is proposed. Eleven impeller flow surfaces are selected as control parameters, and the centrifugal impeller blade profile is redesigned. Numerical simulations and experimental results of the gas quenching furnace’s flow and temperature fields indicate that the modified impeller significantly reduces the blade tip leakage flow strength, with the average blockage coefficient decreasing to 0.07 and 0.04, respectively. The standard deviation of the average flow velocity at the test section is reduced by 42.78% compared to the original, and the temperature fluctuation at the workpiece surface is reduced by 53.09%. Both the flow and temperature field uniformity are significantly improved. Full article

Cutting is a crucial step in the industrial production process, particularly in the manufacture of large structures. In certain spatial positions, using a mobile robot, especially a wall-climbing robot (WCR) with adsorption function, is essential for carrying cutting torches to cut large steel components. The cutting quality directly impacts the overall manufacturing quality. Therefore, effectively tracking the cutting trajectory of wall-climbing cutting robots is very important. This study proposes a controller based on a kinematic model and PID optimization. The controller is designed to manage the robot’s kinematic trajectory, including the torch slider, through the kinematic modeling of the wall-climbing cutting robot (WCCR). The stability of the control law is proven using the Lyapunov function, which controls the linear and angular velocities of the WCCR and the motion speed of the cross slider. Simulations verify that the control law performs well in tracking both straight-line and circular trajectories. The impact of different control law parameters on straight-line trajectory tracking is also compared. By introducing PID optimization control, the controller’s anti-interference capabilities are enhanced, addressing the issue of motion velocity fluctuation when the WCCR tracks curved trajectories. The simulation and experiment results demonstrate the effectiveness of the proposed controller. Full article