Variable Speed Research Articles

Study on the hydrodynamic and wake characteristics of variable speed control of horizontal axis tidal turbine under surge motion

Study on the hydrodynamic and wake characteristics of variable speed control of horizontal axis tidal turbine under surge motion

Enhancing wind power with permanent magnet synchronous generator control strategies

AbstractDue to the substantial rise in wind power generation, the direct-drive permanent magnet synchronous generator has emerged as a leading technology for efficient variable speed operation, meeting grid demands effectively. This paper presents a comparative analysis of control strategies for permanent magnet synchronous generator based wind turbine using real variable wind speed data from a 2 MW of Tetouan wind farm in Morocco. The proposed approach is based on evaluating two primary control strategies: the adaptive fuzzy-proportional-integral controller and the conventional proportional-integral controller aimed at enhancing the wind turbine's output power. The simulation performed on MATLAB-Simulink indicates that pitch control mechanisms play a crucial role in optimizing power generation, also demonstrating its ability to achieve satisfactory performance.

Increasing fluctuations in the Arctic summer sea ice cover are expected with future global warming

The loss of Arctic sea ice (ASI) represents a major transformation in the Arctic region, impacting regional and global climate, ecosystems, and socio-economic structures. Observational and reanalysis data have consistently shown a notable shift in polar environmental conditions over recent decades, marked by a substantial reduction in the ASI area and a rise in the variability in its coverage and distribution. Utilizing data from the latest Coupled Model Intercomparison Project phase, our study reveals a consistent pattern highlighting a fundamental shift in ASI dynamics preceding total loss. We observe increasing fluctuations in the September ASI area as the threshold for an ice-free Arctic is approached across various scenarios and models. This pattern is particularly concentrated in the Central Arctic (CA) sub-region. Spatial analyses reveal increasing variance along the CA’s northern coastlines, accompanied by a substantial increase in open water coverage, underscoring the shift from stable to highly variable ice conditions in this region. Additionally, our findings suggest a potential link between increased ASI fluctuations and variability in surface wind speeds. These specific results underscore the urgency of multidisciplinary approaches in addressing the challenges posed by ASI variability, with implications for marine ecosystems, Indigenous communities, and navigational safety.

Appendicular lean mass index and motor function in ambulatory patients with Duchenne muscular dystrophy.

Appendicular lean mass index (ALMI) has been linked to motor function in patients with Duchenne muscular dystrophy (DMD). However, quantification of the relationship between ALMI and disease-specific clinical outcome assessment trajectories is needed. The purpose of this study was to determine associations between dual-energy x-ray absorptiometry (DXA) derived estimates of ALMI and motor function in ambulatory patients with DMD. A retrospective analysis of longitudinal clinical visit data from 137 glucocorticoid-treated patients with DMD collected via structured motor assessment protocol evaluated associations between ALMI and motor function indexed by the North Star Ambulatory Assessment (NSAA) and 10 Meter Walk/run Test (10MWT). Body composition was assessed using DXA. ALMI was calculated by dividing arm and leg lean mass by height in m2; fat mass index (FMI) was calculated by dividing whole body fat mass by height in m2. Linear mixed-effects models were used to estimate associations between ALMI and motor function, controlling for age and FMI. The full prediction model (age, age,2 ALMI, and FMI) explained 57% of the variance in NSAA scores and 63% of the variance in 10MWT speed. A 1 kg/m2 higher ALMI value predicted a 5.4-point higher NSAA score (p < .001) and 0.45 m/s faster 10MWT speed (p < .001). A 1 kg/m2 higher FMI value predicted a 1.5-point lower NSAA score (p < .001) and 0.14 meters/second slower 10MWT speed (p < .001). DXA-derived estimates of ALMI and FMI are associated with motor function in DMD and may explain variation in DMD disease progression.

Numerical investigation of the vorticity transportation and energy dissipation in a variable speed pump-turbine

Variable speed technology is utilized in pump turbines to enhance their operational efficiency and stability. The unsteady internal flows of a variable speed pump turbine operating at different rotation speeds are numerically investigated with SST k-ω turbulence model. Firstly, the ideal rotating speed is selected for each output. Amongst three researched output levels, the lowest entropy production consistently occurs at minimum rotation speed,1350 rpm. Under similar outputs, higher rotation speeds result in higher entropy production. Difference between various rotation speed is up to 40 %. Secondly, Liutex method is used to structure the vortex while vorticity transport equation is applied to analyze its evolution. Results show that variable speed operation can effectively reduce pressure fluctuation amplitude with approximately 20 %–40 % amplitude drop. Subsequently, the relationship between vortex rope and entropy production in the draft tube is deeply explored as it reveals that entropy production in the draft tube correlates with vortex rope evolution. Moreover, direct dissipation's entropy production and turbulence dissipation's entropy production are two primary components leading to irreversible energy loss. It is clearly revealed that the entropy production reduces with rotation speed. The relationship between entropy production and vortex rope in the draft tube is confirmed.

Study of Two-wheel Vehicle’s fuel consumption under the Influence of tire Inflation Pressure

The value of air pressure in the tire not only supports the entire weight of the vehicle, it also plays a crucial role in improving the vehicle’s performance, reducing friction, providing comfort, boosting economy and enhancing safety. This study deals with the influence of tire inflation pressure on fuel consumption at various road conditions and speeds. The experimental test was performed riding a motorcycle of 180cc and using a mileage measuring kit to find the effect of tire pressure on three different road surfaces i.e., highway, earthen and graveled. The standard tire pressure for both front and rear tires recommended by the manufacturing company was also evaluated to observe the change in mileage. The results showed that the highest mileage of 55.97 km/L was obtained on the highway among the defined three road conditions and a linear drop of 5psi in tire pressure led to loss of mileage up to 6.11% in highway, 5.35% in earthen and 2.80% in graveled road. Also, the variation in speed resulted to compensation of mileage by nearly 39.9% maximum. These results highlight the importance of keeping the correct value of tire inflation pressure in relation to fuel consumption.

Review of Fault Diagnosis Methods for Induction Machines in Railway Traction Applications

Induction motors make up approximately 80% of the electric motors in the railway sector due to their robustness, high efficiency, and low maintenance cost. Nevertheless, these motors are subject to failures which can lead to costly downtime and service interruptions. In recent years, there has been a growing interest in developing fault diagnosis systems for railway traction motors using advanced non-invasive detection and data analysis techniques. Implementing these methods in railway applications can prove challenging due to variable speed and low-load operating conditions, as well as the use of inverter-fed motor drives. This comprehensive review paper summarizes general methods of fault diagnosis for induction machines. It details the faults seen in induction motors, the most relevant signals measured for fault detection, the signal processing techniques for fault extraction as well as some classification algorithms for diagnosis purposes. By giving the advantages and drawbacks of each technique, it helps select the appropriate method that could address the challenges of railway applications.

Influence of Tire-Enveloping Model Complexity on High-Frequency Simulations

ABSTRACT Race tires are subject to high speeds and high vertical loads during cornering. They are also subject to extreme stress when they are driven over high-frequency and low-amplitude curbs, often leading to tire failures. Research and development by D. C. Davis and P. W. A. Zegelaar has shown that when the tire moves over high-frequency, low-amplitude, and short-wavelength obstacles, it exhibits obstacle-enveloping properties. Tire response over such obstacles includes vertical force variation, longitudinal force variation, and rotational speed variation. Additionally, such road inputs excite tire belt dynamics that include high-frequency modes. Therefore, a single-contact-point model is no longer sufficient to predict tire response over such obstacles. To better capture the tire obstacle-enveloping behavior, advanced contact patch models such as the tandem-cam model by P. W. A. Zegelaar, the radial–interradial spring model by J. M. Badalmenti and G. R. Davis Jr., and the flexible ring model by S. Gong have been developed. To capture high-frequency tire belt dynamics, the rigid-ring model has been developed by P. W. A. Zegelaar. Previous work combines the rigid-ring model with the tandem-cam model to capture obstacle-enveloping behavior and tire belt dynamics up to 60–100 Hz. This paper presents a comparative study of different tire–road contact models to simulate high-frequency curbs typically found at racetracks. The curb profile of the Singes corner at the Paul Ricard racetrack is chosen for simulations as it is known to be extremely demanding on the tires, often causing failures. An advanced four-wheel model is used to simulate a race car at high speeds. Metrics to quantify the tire response over these high-frequency curbs are created and an investigation is conducted to quantify the influence of tire model complexity on the metrics.

Exploring interacting effects of risk factors on run-off-road crash severity: An interpretable machine learning model joint with latent class clustering

Run-Off-Road (ROR) crashes are frequent and pose a significant risk of injury and fatality. Given the complexity of their mechanisms and the interaction of multiple factors, this study aims to comprehensively investigate the factors influencing the severity of ROR crashes, which have been understudied. Furthermore, addressing the current methodological challenge in machine learning (ML) crash modeling, this study proposes an approach to tackle unobserved heterogeneity in ML. An interpretable ML joint with prior latent class clustering is implemented. The significant risk factors and interactions are interpreted using SHAP (SHapley Additive exPlanations) method across clusters. This study utilizes ROR crash records, traffic, and geometric data from main suburban freeways in Iran collected over a 5-year period. The key interacting factors associated with severe ROR during adverse weather (cluster 1) are: co-occurrence of low congestion and higher speed variation; low congestion, nighttime darkness and rollover; roadway departure by buses and mini-buses and rollover occurrence; vehicle departure and collision with fixed objects. Moreover, the critical interactions for nighttime condition (cluster 2) are: curve sections combined with longitudinal slope; inside shoulder width <1.5 m and a hit median concrete NewJersey barrier. The risky interactions for crashes occurred in curves (cluster 3) are: departing in two-lane sections in low congestion conditions; vehicles collisions with the median concrete NewJersey barriers. The findings of this study enhance comprehension of the significant effects of interactions under various conditions, offering valuable insights for policymakers. Additionally, recommendations are offered to mitigate the risk of severe ROR crashes.

Intelligent Sensing of Thermal Error of CNC Machine Tool Spindle Based on Multi-Source Information Fusion.

Aiming at the shortcomings of single-sensor sensing information characterization ability, which is easily interfered with by external environmental factors, a method of intelligent perception is proposed in this paper. This method integrates multi-source and multi-level information, including spindle temperature field, spindle thermal deformation, operating parameters, and motor current. Firstly, the internal and external thermal-error-related signals of the spindle system are collected by sensors, and the feature parameters are extracted; then, the radial basis function (RBF) neural network is utilized to realize the preliminary integration of the feature parameters because of the advantages of the RBF neural network, which offers strong multi-dimensional solid nonlinear mapping ability and generalization ability. Thermal-error decision values are then generated by a weighted fusion of different pieces of evidence by considering uncertain information from multiple sources. The spindle thermal-error sensing experiment was based on the spindle system of the VMC850 (Yunnan Machine Tool Group Co., LTD, Yunnan, China) vertical machining center of the Yunnan Machine Tool Factory. Experiments were designed for thermal-error sensing of the spindle under constant speed (2000 r/min and 4000 r/min), standard variable speed, and stepped variable speed conditions. The experiment's results show that the prediction accuracy of the intelligent-sensing model with multi-source information fusion can reach 98.1%, 99.3%, 98.6%, and 98.8% under the above working conditions, respectively. The intelligent-perception model proposed in this paper has higher accuracy and lower residual error than the traditional BP neural network perception and wavelet neural network models. The research in this paper provides a theoretical basis for the operation, maintenance management, and performance optimization of machine tool spindle systems.

Deep Q‐network learning‐based active speed management under autonomous driving environments

AbstractEfficient traffic safety management necessitates real‐time crash risk prediction using expressway characteristics. With the emergence of autonomous vehicles (AVs), the development and evaluation of variable speed limit (VSL) strategies, a key active traffic management technique, become crucial for enhancing safety and mobility in mixed traffic flows. This underscores the need for optimized VSL strategies to accommodate both conventional and AVs. This paper presents a study on the development of VSL control algorithms using deep reinforcement learning in a microscopic traffic simulation. As the rewards function, time‐to‐collision and speed were considered. To enhance traffic safety, VSL strategies were refined across various market penetration of connected AVs. Analysis revealed that safety and traffic density are improved by 53% and 59%, respectively, in market penetration rate (MPR) 50, marking significant safety improvements in congested and low MPR scenarios. These findings present the importance of developing and evaluating VSL strategies for mixed traffic flow, particularly in the context of increasing the prevalence of connected and AVs.

ASG-YOLOv5: Improved YOLOv5 unmanned aerial vehicle remote sensing aerial images scenario for small object detection based on attention and spatial gating.

With the accelerated development of the technological power of society, aerial images of drones gradually penetrated various industries. Due to the variable speed of drones, the captured images are shadowed, blurred, and obscured. Second, drones fly at varying altitudes, leading to changing target scales and making it difficult to detect and identify small targets. In order to solve the above problems, an improved ASG-YOLOv5 model is proposed in this paper. Firstly, this research proposes a dynamic contextual attention module, which uses feature scores to dynamically assign feature weights and output feature information through channel dimensions to improve the model's attention to small target feature information and increase the network's ability to extract contextual information; secondly, this research designs a spatial gating filtering multi-directional weighted fusion module, which uses spatial filtering and weighted bidirectional fusion in the multi-scale fusion stage to improve the characterization of weak targets, reduce the interference of redundant information, and better adapt to the detection of weak targets in images under unmanned aerial vehicle remote sensing aerial photography; meanwhile, using Normalized Wasserstein Distance and CIoU regression loss function, the similarity metric value of the regression frame is obtained by modeling the Gaussian distribution of the regression frame, which increases the smoothing of the positional difference of the small targets and solves the problem that the positional deviation of the small targets is very sensitive, so that the model's detection accuracy of the small targets is effectively improved. This paper trains and tests the model on the VisDrone2021 and AI-TOD datasets. This study used the NWPU-RESISC dataset for visual detection validation. The experimental results show that ASG-YOLOv5 has a better detection effect in unmanned aerial vehicle remote sensing aerial images, and the frames per second (FPS) reaches 86, which meets the requirement of real-time small target detection, and it can be better adapted to the detection of the weak and small targets in the aerial image dataset, and ASG-YOLOv5 outperforms many existing target detection methods, and its detection accuracy reaches 21.1% mAP value. The mAP values are improved by 2.9% and 1.4%, respectively, compared with the YOLOv5 model. The project is available at https://github.com/woaini-shw/asg-yolov5.git.

Capacitance Energy Control for FESSs: Tracking Performance Analysis and Robust Design Against Variations of Speed, Load, and Capacitance

Capacitance Energy Control for FESSs: Tracking Performance Analysis and Robust Design Against Variations of Speed, Load, and Capacitance

Modeling the effect of wind speed and direction shear on utility‐scale wind turbine power production

AbstractWind speed and direction variations across the rotor affect power production. As utility‐scale turbines extend higher into the atmospheric boundary layer (ABL) with larger rotor diameters and hub heights, they increasingly encounter more complex wind speed and direction variations. We assess three models for power production that account for wind speed and direction shear. Two are based on actuator disc representations, and the third is a blade element representation. We also evaluate the predictions from a standard power curve model that has no knowledge of wind shear. The predictions from each model, driven by wind profile measurements from a profiling LiDAR, are compared to concurrent power measurements from an adjacent utility‐scale wind turbine. In the field measurements of the utility‐scale turbine, discrete combinations of speed and direction shear induce changes in power production of −19% to +34% relative to the turbine power curve for a given hub height wind speed. Positive speed shear generally corresponds to over‐performance and increasing magnitudes of direction shear to greater under‐performance, relative to the power curve. Overall, the blade element model produces both higher correlation and lower error relative to the other models, but its quantitative accuracy depends on induction and controller sub‐models. To further assess the influence of complex, non‐monotonic wind profiles, we also drive the models with best‐fit power law wind speed profiles and linear wind direction profiles. These idealized inputs produce qualitative and quantitative differences in power predictions from each model, demonstrating that time‐varying, non‐monotonic wind shear affects wind power production.

Three-phase model of SCIG-based variable speed wind turbine for unbalanced DSLF analysis

Steady state performances of the electric power distribution system are normally assessed or evaluated based on load flow analysis. To properly carry out the analysis, a valid steady state load flow model of each distribution system component, including the wind power plant (WPP), needs to be developed. The present paper proposes a method for modeling and integrating squirrel cage induction generator (SCIG)-based variable speed WPP into a three-phase unbalanced distribution system load flow (DSLF) analysis. The proposed method is based on a single-phase T-circuit model of fixed speed WPP, which has successfully been applied to balanced electric power systems. In the present work, the single-phase T-circuit model is extended and modified to be used in steady state load flow analysis of three-phase unbalanced distribution systems embedded with SCIG-based variable speed WPP. Results of the case studies confirm the validity of the proposed method.

Applying Sliding Mode Control Along with Particle Swarm Algorithm in Order to Optimally Control the System Wind Turbines with Variable Speed

Applying Sliding Mode Control Along with Particle Swarm Algorithm in Order to Optimally Control the System Wind Turbines with Variable Speed

Exploring the utility of process scores in elucidating the role of cognitive and affective factors that influence verbal fluency performance in Parkinson’s disease

Objectives Cognitive and affective factors have been implicated in verbal fluency (VF) performance in Parkinson’s disease (PD). This exploratory study aimed to investigate the relationships between cognitive and affective variables on traditional (“core”) and "process” (error and interval) scores of VF and elucidate unique information these scores may provide regarding mechanisms underlying VF. Methods Sixty-two PD patients without dementia completed clinical neuropsychological examinations consisting of attention, processing speed, language, executive functioning, visuospatial, memory, and mood measures. Hierarchical regression and negative binomial regression analyses were used to evaluate relationships between outcome and predictor variables. Results Generativity results revealed that processing speed and working memory explained up to 34% of the variance of total letter fluency responses (p = <.001) and processing speed explained 24% of the variance for total semantic fluency (p = .003). For category switching generativity, only age predicted 20% of the variance (p = .01). Two executive functioning measures were negatively associated with error production over the duration (b = −.055, p = .028; b = −.062, p = .004) and final 45-second interval (b = −.072, p = .003; b = −.044, p = .033) of the category switching task. In the initial 15-second task interval, a positive predictive relationship between error production and indifference apathy (b = .616, p = .044) was demonstrated. Conclusions Findings demonstrate the potential utility of "process” scores in detecting subtle cognitive impairment in Parkinson’s disease patients without dementia and tentatively evidence the role of indifference apathy in task initiation.

Real-Time Surface Identification System for Variable Walking Speeds of Biped Robots

Real-Time Surface Identification System for Variable Walking Speeds of Biped Robots

A Taguchi method-based optimization algorithm for the analysis of the wind driven-self-excited induction generator

This paper investigates the use of a new global optimization algorithm that is based on Taguchi method to determine the performance parameters of self-excited induction generator being driven by variable speed wind. This analysis is based on solving equations obtained from the per-phase equivalent circuit of the induction generator. The equations have two unknowns namely the frequency and the magnetizing reactance. Both unknown are strongly dependent on the wind turbine speed, the capacity of the excitation, the load being connected at the terminals of the stator and eventually the per-phase equivalent circuit parameters. The resulting equations are nonlinear and subsequently to solve them one can employ either gradient-based algorithms or heuristic algorithms. This paper uses a new heuristic algorithm based on the Taguchi method which, in addition to its global research capability, offers superior characteristics in terms of accuracy and ease of implementation. A comparison with recently published optimization methods is carried out to show its performances in terms of accuracy and ease of implementation. The MATLAB software will be used to perform this analysis on a machine of 0.75 kW while some will be validated experimentally to confirm the aforementioned benefits.

Nonlinear Loads Modelling and Harmonics Analysis: A Review

Abstract: In modern life, the applications of nonlinear loads are increasing, especially electronic loads, such as computers, monitors, and battery chargers. Moreover, the usage of semiconductors has increased in modern lighting systems, such as LED lamps and dimmers which are used to control the lighting level. Nonlinear loads may also be in the form of variable speed drive or arc furnace loads. Due to the spread of nonlinear loads, large amounts of harmonics are generated. To study the harmonics of nonlinear loads, it is essential to represent nonlinear loads by their equivalent models in the simulation programs. This paper presents a comprehensive review of modeling methods of nonlinear loads, including time domain methods, frequency domain methods, and state space methods. Equivalent circuits are presented for different types of nonlinear loads, including static loads, dynamic loads and arc furnace loads. The paper also presents the harmonic analysis performed by the Fourier transform of different types of nonlinear loads, including harmonic percentage and its angle. This paper is considered as a comprehensive reference for researchers and engineers to model nonlinear loads and is useful in power system planning for connecting nonlinear loads where mixing nonlinear loads of similar harmonic profiles causes worse situations. Moreover, knowing the nonlinear load profile will help in the suitable mixing of linear loads to achieve lower harmonics levels.