Search Results (89)

The vibration monitoring of scaled models in wind tunnels is critical for aerodynamic testing and structural safety. The abrupt onset of flutter or other aeroelastic instabilities poses a significant risk, necessitating the development of real-time, model-agnostic monitoring systems. This paper proposes a novel, generalized health indicator (HI) based on an improved Fisher Discriminant Analysis (FDA) framework for vibration state classification. The core innovation lies in reformulating the FDA objective function to distinguish between stable and dangerous vibration states, rather than tracking degradation trends. To ensure cross-model applicability, a frequency-wise standardization technique is introduced, normalizing spectral amplitudes based on the statistics of a model’s stable state. Furthermore, a dual-mode entropic regularization term is incorporated into the optimization process. This term balances the dispersion of weights across frequency bands (promoting generalizability and avoiding overfitting to specific frequencies) with the concentration of weights on the most informative resonance frequencies (enhancing the sensitivity to dangerous states). The optimal frequency weights are obtained by solving a regularized generalized eigenvalue problem, and the resulting HI is the weighted sum of the standardized frequency amplitudes. The method is validated using simulated spectral data and flight data from a wind tunnel test, demonstrating a superior performance in the early detection of dangerous vibrations and the clear interpretability of critical frequency bands. Comparisons with traditional sparse measures and machine-learning methods highlight the proposed method’s advantages in trendability, robustness, and unique capability for cross-model adaptation. Full article

Permanent magnet synchronous generators (PMSGs) are suitable for offshore applications due to their high efficiency and power density. Inter-turn short circuits (ITSCs) stand as one of the most critical faults in these machines due to their rapid evolution in phase or ground short circuits. It is therefore necessary to detect ITSCs at an early stage. In the literature, ITSC detection is often based on current signal processing methods. One of the challenges that these methods face is the presence of imperfections in the stator coils, which also affects the three-phase symmetry. Moreover, when the stator coils are connected in parallel, this type of fault becomes important, as circulating currents will flow between the parallel windings. This, in turn, increases the thermal stress on the insulation and the permanent magnets, while also exacerbating the vibrations of the generator. In this study, a finite-element analysis (FEA) model has been developed to simulate a dual-rotor PMSG under conditions of coil asymmetry. To further investigate the impact of this asymmetry, mathematical modeling has been conducted. For fault detection, negative-sequence current (NSC) analysis and torque monitoring have been used to distinguish coil asymmetry from ITSCs. While both methods demonstrate potential for fault identification, NSC induced small amplitudes and the torque analysis was unable to detect ITSCs under low-severity conditions, thereby underscoring the importance of developing advanced strategies for early-stage ITSC detection. The innovative aspect of this work is that, despite these limitations, the combined use of NSC phase-angle tracking and torque harmonic analysis provides, for the first time in a core-less PMSG with parallel-connected coils, a practical way to distinguish ITSC from coil asymmetry, even though both faults produce almost identical signatures in conventional current-based indices. Full article

The scaling effect of machines from kW to MW greatly affects electromagnetic performance and needs to be investigated for different machines. Therefore, this paper presents a comprehensive comparative study on the intriguing electromagnetic performance of contra-rotating permanent-magnet vernier machines and dual-port, wound-field-excited, flux-switching machines at the MW power level for contra-rotating wind turbine applications. The analysis evaluates both machines across various slot/pole combinations while maintaining constant key design parameters. The electromagnetic performance analysis reveals that the permanent-magnet vernier machine (PMVM) exhibits superior torque and power, with minimal cogging torque compared to the wound-field flux-switching machine (WFFSM). Conversely, the WFFSM outperforms the PMVM in terms of power factor and efficiency. This study provides valuable perspectives on the strengths and weaknesses of each machine, highlighting their potential for contra-rotating turbine and wind power generation. Finally, to justify the findings of the finite element analysis and the proof of concept, an experimental prototype is tested to validate the study. Full article

Spaceborne Synthetic Aperture Radar (SAR) can be applied for monitoring tropical cyclones (TCs), but co-polarized C-band SAR suffers from signal saturation such that it is improper for high wind-speed conditions. In contrast, cross-polarized SAR data does not suffer from this issue, but the retrieval algorithm needs more deliberation. Previously, many geophysical model functions (GMFs) have been developed using cross-polarized data, which obtain wind speeds using the complex relationships described by radar backscatter, incidence angle, wind direction, and radar look direction. In this regard, the rapid development of artificial intelligence technology has provided versatile machine learning methods for such a nonlinear inversion problem. In this study, we comprehensively compare the wind-speed retrieval performance of several models including Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN), which were developed based on spatio-temporal matching and correlation analysis of stepped frequency microwave radiometer (SFMR) and dual-polarized Sentinel-1 SAR data after noise removal. A data set with ~2800 samples is generated during TCs for training and validating the inversion model. The generalization ability of different models is tested by the reserved independent data. When using similar parameters with GMFs, RF inversion has the highest accuracy with a Root Mean Square Error (RMSE) of 3.40 m/s and correlation coefficient of 0.94. Furthermore, considering that the sea surface temperature is a crucial factor for generating TCs and influencing ocean backscattering, its effects on the proposed RF model are also explored, the results of which show improved wind-speed retrieval performances. Full article

Waste prevention is at the top of the EU Waste Framework directive hierarchy. With this in mind, this article considers the application of novel technologies in the Cultural Heritage Restoration and Conservation field through environmental and circular economy principles. While previous research has explored the use of wood waste for composite materials such as building insulation and concrete additives, the suitability of degraded historical wood waste for filament production and 3D printing has not yet been addressed. This article contributes to this topic by studying the PLA/wood composite, material composed of a polylactic acid (PLA) polymer matrix reinforced with wood particles, produced from degraded historical construction materials. The paper describes the process of producing filament from bio- and moisture-damaged pine beam and oak parquet, followed by the 3D printing of historical platband replica. Research methods include photogrammetry, filament machine construction, filament production and 3D printing. The machines settings used in the process: heater temperatures were set to 140 °C, 90 °C and 105 °C; servo speed was 33 s; spool tension was 12.5; winding speed was 24 RPM; and screw speed was 9.2 RPM. For material preparation, a mixture containing 25% pine and oak sawdust and PLA dust was processed to achieve particle sizes of 312 μm, 471 μm, and 432 μm, respectively. Filament production was carried out with diameters of 2.85 mm for the pine/PLA composite and 1.75 mm for the oak/PLA composite. Finally, replica samples were fabricated using 3D printing. The dual objective of this research was to develop the method of 3D printing from degraded historical materials and introduce it to restoration practice as a wood waste minimization technique. Perspectives for further study include the testing of 3D-printed construction materials in outdoor conditions, and pellet production to achieve a higher wood content, compared to the filament thread. The processes described are adaptable to a variety of materials and disciplines. Full article

Auxiliary teeth are usually used in fractional-slot concentrated winding (FSCW) machines for fault tolerance. However, the influence of auxiliary teeth on torque and electromagnetic vibration performance differs with different slot–pole configurations. Thus, this paper investigates electromagnetic vibration and mitigation methods in FSCW permanent magnet (PM) machines with auxiliary teeth. The relationship between yoke forces and tooth parameters of two dual three-phase (DTP) FSCW-PM machines with 12-slot/14-pole configuration and 12-slot/10-pole configuration is studied and compared. Results reveal that (1) the 2p-order airgap electromagnetic force reduces second-order yoke force in the 12-slot/14-pole machine but increases it in the 12-slot/10-pole machine. (2) Through optimized tooth width, slot harmonics can be mitigated, but the fundamental winding magnetic field in the 12-slot/10-pole machine is also weakened, whereas the 12-slot/14-pole machine achieves fundamental field preservation or enhancement. Based on these findings, auxiliary tooth optimization and rotor pole profile shaping are proposed for vibration reduction in 12-slot/14-pole machine. Electromagnetic–mechanical coupled simulations conducted in ANSYS Maxwell/Workbench 2023 demonstrate that the optimized design reduces the cogging torque peak from 11.4 mN·m to 2.9 mN·m (74.6% reduction), suppresses housing surface vibration acceleration by 21%, and maintains the average output torque without reduction. Full article

In the context of global climate change and accelerated urbanization, coastal cities face severe threats from storm surges, and accurately predicting coastal water level changes during storm surges has become a core technological demand for disaster prevention and reduction. Storm surges are caused by atmospheric pressure and wind conditions, and their destructive power is closely related to the morphology of the coastline. Traditional tide level prediction models often face difficulties in boundary condition parameterization. Tide level changes result from the combined effect of various complex processes. In past prediction studies, harmonic analysis and numerical simulations have dominated, each with their own limitations. Although machine learning applications in tide prediction have garnered attention, issues such as data inconsistency or missing data still exist. The physical–data fusion approach aims to overcome the limitations of single methods but still faces some challenges. This paper proposes a Deep-Numerical-Reinforcement learning fusion prediction model (DNR), which adopts ensemble learning. First, deep learning models and the numerical model Finite-Volume Coastal Ocean Model (FVCOM) are used to predict tide levels at different tide stations, and then a fusion approach based on the improved reinforcement learning model DDPG_dual is applied for model assimilation. This reinforcement learning fusion model includes a module specifically designed to handle tide extreme points. In the case of the Typhoon Mangkhut storm surge, the DNR model achieved the best results for tide level predictions at six tide stations in the South China Sea. Full article

In this paper, a method for determining the optimal stator-rotor combinations of conventional flux-switching permanent magnet (FSPM) machines with composite phase numbers covering symmetrical and asymmetrical topologies is proposed by changing the equivalent number of coils per pole per phase (ENCPP) or the number of coil-pairs having complementarity (K) of the optimal stator-rotor combinations of the corresponding machines with prime phases. Taking composite phase machines such as four-phase, six-phase, nine-phase, and twelve-phase machines as examples, a detailed analysis is conducted on how the optimal stator-rotor combinations of four-phase machines are derived from the optimal stator-rotor combinations of the corresponding prime phase machines (i.e., two-phase machines) and how the optimal stator-rotor combinations of six-phase, nine-phase, and twelve-phase machines are derived from the optimal stator-rotor combinations of the corresponding prime phase machines (i.e., three-phase machines). Then, the winding factor of the conventional FSPM machines with composite phase numbers is calculated. Finally, taking a 24-slot/22-tooth (24/22) conventional FSPM topology as an example, the topology is connected into a standard six-phase machine (symmetrical topology) and a dual three-phase machine (asymmetrical topology), and a comparative study between them is conducted in terms of the phase back electromotive force (EMF) waveform, electromagnetic torque, torque ripple, and inductances. The results indicate that both machines have sufficiently large and symmetrical back-EMFs, as well as sufficiently large electromagnetic torque, which validates the correctness of the proposed method for determining the optimal stator-rotor combinations. Full article

The integration of large-scale wind power clusters significantly reduces the inertia level of the power system, increasing the risk of frequency instability. Accurately assessing the equivalent virtual inertia of wind farms is critical for grid stability. Addressing the dual bottlenecks in existing inertia assessment methods, where physics-based modeling requires full control transparency and data-driven approaches lack interpretability for inertia response analysis, thus failing to reconcile commercial confidentiality constraints with analytical needs, this paper proposes a symbolic regression framework for inertia evaluation in doubly fed wind farms with limited control information constraints. First, a dynamic model for the inertia response of DFIG wind farms is established, and a mathematical expression for the equivalent virtual inertia time constant under different control strategies is derived. Based on this, a nonlinear function library reflecting frequency-active power dynamic is constructed, and a symbolic regression model representing the system’s inertia response characteristics is established by correlating operational data. Then, sparse relaxation optimization is applied to identify unknown parameters, allowing for the quantification of the wind farm’s equivalent virtual inertia. Finally, the effectiveness of the proposed method is validated in an IEEE three-machine nine-bus system containing a doubly fed wind power cluster. Case studies show that the proposed method can fully utilize prior model knowledge and operational data to accurately assess the system’s inertia level with low computational complexity. Full article

This paper introduces a novel field-oriented control (FOC) strategy for an open-end stator three-phase winding induction motor (OEW-TP-IM) using dual space vector modulation-pulse width modulation (SVM-PWM) inverters. This configuration reduces common mode voltage at the motor’s terminals, enhancing efficiency and reliability. The study presents a backstepping control approach combined with a mean value theorem (MVT)-based observer to improve control accuracy and stability. Stability analysis of the backstepping controller for key control loops, including flux, speed, and currents, is conducted, achieving asymptotic stability as confirmed through Lyapunov’s methods. An advanced observer using sector nonlinearity (SNL) and time-varying parameters from convex theory is developed to manage state observer error dynamics effectively. Stability conditions, defined as linear matrix inequalities (LMIs), are solved using MATLAB R2016b to optimize the observer’s estimator gains. This approach simplifies system complexity by measuring only two line currents, enhancing responsiveness. Comprehensive simulations validate the system’s performance under various conditions, confirming its robustness and effectiveness. This strategy improves the operational dynamics of OEW-TP-IM machine and offers potential for broad industrial applications requiring precise and reliable motor control. Full article

Today, power electronic-based converters are at the core of many modern systems, such as smart grids and electric vehicles. In this context, the Dual Two-Level Inverter (DTLI) supplying an open-end winding machine offers an innovative and promising solution for marine propulsion, aeronautics, and electric vehicles. This configuration provides several advantages, including a reduced DC bus voltage, enhanced fault tolerance, and improved overall system performance. However, ensuring optimal energy efficiency and high-power quality remains a major challenge given the increasing demands for performance and sustainability. This paper presents a state-of-the-art review of the main DTLI configurations and their impact on system performance. Three architectures are analyzed, highlighting their benefits and limitations. This study aims to demonstrate the influence of the DC bus voltage ratio and pulse width modulation strategies on power quality and energy efficiency. The objective is to enhance the understanding of the DTLI’s potential and to guide its integration into other electrical systems. Full article

This paper proposes a hybrid brushless wound-rotor synchronous machine (HB-WRSM) with an outer rotor topology that can operate as a permanent magnet synchronous machine (PMSM), as well as an HB-WRSM. In the first part, the existing brushless wound-rotor synchronous machine (BL-WRSM) is modified into a hybrid model by introducing permanent magnets (PMs) in the rotor pole faces to improve the magnetic field strength and other performance variables of the machine. In the second part, a centrifugal switch is introduced, which can change the machine operation from HB-WRSM to PMSM. The proposed machine uses an inner stator, outer rotor model with 36 stator slots and 48 poles, making the stator winding a concentrated winding. The HB-WRSM is utilized for dual-speed applications such as washing machines that run at low speed (46 rpm) and high speed (1370 rpm). For high speed, to have a better efficiency and less torque ripple, the machine is switched to PMSM mode using a centrifugal switch. The results are compared with the existing BL-WRSM. A 2D model is simulated using ANSYS Electromagnetics Suite to validate the machine model and performance. Full article

The marine environment’s complexity poses considerable difficulties for the stability and reliability of communication links. The restricted coverage of onshore base stations in marine areas makes relay technology a critical solution for extending the communication coverage. Here, connectivity analyses help nodes select the optimal forwarding links, reducing transmission failures and improving the network performance. However, the rapid changes in marine wireless channels and the complexity of hydrological conditions make it challenging to acquire precise channel state information (CSI). In particular, dynamic environmental factors like tides, waves, and wind speed lead to substantial variations in the channel parameters over time. In response to these challenges, this paper puts forward a ship-to-shore communication system using relay ships to extend the coverage of terrestrial base stations. A novel channel modeling method is designed to capture the characteristics of marine wireless channels accurately. Additionally, a machine learning (ML)-based approach is introduced to predict the dual-hop link connection probability at future time points by analyzing historical time-series data on oceanic environmental and ship movement parameters. The proposed model consists of a convolutional-layer-based feature extractor and a bidirectional long short-term memory (BiLSTM) estimator. The CNN module extracts effective high-level features from the input data, while the BiLSTM module further explores the dependencies and dynamic patterns along the temporal dimension. The attention mechanism is introduced to distinguish the importance of the information through a weighted approach. The experimental results show that compared to traditional methods and other deep learning approaches, the proposed CNN-BiLSTM-AM model performs better in terms of its prediction accuracy and fitting ability. The model’s mean squared error (MSE) is as low as 0.0126. Full article

The dual-stator permanent magnet (DSPM) machine has proved to have high space utilization and a redundant structure, which can be beneficial to improving the fault tolerance and torque density performance. In this paper, three types of DSPM machines are proposed and compared, where two sets of armature windings are wound in both inner/outer stators, producing more than one torque component compared with single-stator PM machines. The machine topology and operating principle of three DSPM machines are analyzed first. Next, feasible stator/rotor-pole number combinations are compared and determined. Furthermore, based on the finite-element (FE) method, both the electromagnetic performances of three DSPM machines under open-circuit and rated-load conditions after optimization are compared, aimed at generating maximum torque at fixed copper loss. The FE analyses indicate that the dual-stator consequent-pole permanent magnet (DSCPPM) machine generates maximum torque per PM volume, together with relatively high efficiency, which makes it a potentially hopeful candidate for low-speed and high-torque applications. In addition, a thermal analysis is carried out to confirm the validity of the design scheme. Finally, in order to verify the FE predictions, a prototype DSCPPM machine is manufactured and experimentally tested. Full article

This study employs two distinct machine learning (ML) methodologies to investigate the impact of 12 different key climatic variables on wheat production efficiency, a crucial component of the global and Turkish agricultural economy. Neural network (NN) and eXtreme Gradient Boosting (XGBoost) algorithms are utilised to model wheat production performance using climate variable data, including greenhouse gases, from 1990 to 2024. The models incorporate a total of 21 different independent variables, comprising 9 climatic variables (daytime and nighttime total 18 variables) and 3 distinct greenhouse gas variables, considering day and night values separately. Wheat production efficiency analyses indicate that between 2005 and 2024, Turkey’s wheat cultivation area decreased, while production efficiency increased. ML analyses reveal that greenhouse gases are the most influential variables in wheat production. XGBoost identified four different variables associated with wheat production, whereas the neural network determined that five different variables affect wheat production. While the influence of greenhouse gases was observed in both ML models, it was concluded that nighttime humidity, daytime 10 m v-wind, and daytime 2 m temperature may be additional climatic factors that will impact wheat production in the future. This study elucidates the complex relationship between climate change and wheat production in Turkey. The findings emphasise the importance of the potential for predicting wheat yields with the dual influence of climatic factors and informing agricultural producers about such next-generation practices. Full article