Search Results (460)

Complex mechanical systems, such as agricultural combine harvesters, are subjected to dynamic excitations from multiple coupled sources, compromising structural integrity and operational reliability. Disentangling these vibrations to identify dominant sources and quantify their transmission paths remains a significant engineering challenge. This study proposes a robust diagnostic framework to address this issue. We employed a multi-condition vibration test with sequential source activation and an improved Operational Transfer Path Analysis (OTPA) method. Applied to a harvester chassis, the results revealed that vibration energy is predominantly concentrated in the 0–200 Hz frequency band. Path contribution analysis quantified that the “cutting header → conveyor trough → hydraulic cylinder → chassis frame” path is the most critical contributor to vertical vibration, with a vibration acceleration level of 117.6 dB. Further analysis identified the engine (29.3 Hz) as the primary source for vertical vibration, while lateral vibration was mainly attributed to a coupled resonance between the threshing cylinder (58 Hz) and the engine’s second-order harmonic. This study’s theoretical contribution lies in validating a powerful methodology for vibration source apportionment in complex systems. Practically, the findings provide direct, actionable insights for targeted structural optimization and vibration suppression. Full article

Magnetic gears (MGs) are attracting increasing attention in power transmission systems due to their contactless operation principles, low frictional losses, and high efficiency. However, the broad application potential of these technologies requires a comprehensive evaluation of engineering parameters, such as material selection, energy efficiency, and structural design. This review focuses solely on solid-core magnetic gear systems designed using laminated electrical steels, soft magnetic composites (SMCs), and high-saturation alloys. This review systematically examines the topological diversity, torque transmission principles, and the impact of various core materials, such as electrical steels, soft magnetic composites (SMCs), and cobalt-based alloys, on the performance of magnetic gear systems. Literature-based comparative analyses are structured around topological classifications, evaluation of material properties, and performance analyses based on losses. Additionally, the study highlights that aligning material properties with appropriate manufacturing methods, such as powder metallurgy, wire electrical discharge machining (EDM), and precision casting, is essential for the practical scalability of magnetic gear systems. The findings reveal that coaxial magnetic gears (CMGs) offer a favorable balance between high torque density and compactness, while soft magnetic composites provide significant advantages in loss reduction, particularly at high frequencies. Additionally, application trends in fields such as renewable energy, electric vehicles (EVs), aerospace, and robotics are highlighted. Full article

Permanent magnetic couplings provide critical advantages for deep-sea systems through static-sealed, contactless power transmission. However, conventional metallic isolation sleeves incur significant eddy current losses, limiting efficiency and high-speed operation. Limited torque capacities fail to meet the operational demands of harsh marine environments. This study presents a novel permanent magnet coupling featuring a ceramic isolation sleeve engineered for deep-sea cryogenic ammonia submersible pumps. The ceramic sleeve eliminates eddy current losses and provides exceptional corrosion resistance in acidic/alkaline environments. To withstand 3.5 MPa hydrostatic pressure, a 6-mm-thick sleeve necessitates a 10 mm operational air gap, challenging magnetic circuit efficiency. To address this limitation, an improved 3D magnetic equivalent circuit (MEC) model was developed that explicitly accounts for flux leakage and axial end-effects, enabling the accurate characterization of large air gap fields. Leveraging this model, a Taguchi method-based optimization framework was implemented by balancing key parameters to maximize the torque density. This co-design strategy achieved a 21% increase in torque density, enabling higher torque transfer per unit volume. Experimental validation demonstrated a maximum torque of 920 Nm, with stable performance under simulated deep-sea conditions. This design establishes a new paradigm for high-power leak-free transmission in corrosive, high-pressure marine environments, advancing applications from deep-sea propulsion to offshore energy systems. Full article

Transmission towers constitute critical power infrastructure, yet structural damage may accumulate over their long-term service, underscoring the paramount importance of research on damage identification. This paper presents a cross-correlation function amplitude vector (CorV) method for damage localization based on time-domain response analysis. The approach involves calculating the CorV of structural members before and after damage using dynamic response data, employing the CorV assurance criterion (CVAC) to quantify changes in CorV, and introducing first-order differencing for damage localization. Taking an actual transmission tower in Jiangmen as the engineering backdrop, a finite element model is established. Damage conditions are simulated by reducing the stiffness of specific members, and parameter analyses are conducted to validate the proposed method. Furthermore, experimental validation in a lab is performed to provide additional confirmation. The results indicate that the CVAC value of the damaged structure is significantly lower than that in the healthy state. By analyzing the relative changes in the components of CorV, the damage location can be accurately determined. Notably, this method only requires acquiring the time-domain response signals of the transmission tower under random excitation to detect both the existence and location of damage. Consequently, it is well suited for structural health monitoring of transmission towers under environmental excitation. Full article

Integrating variable renewable energy sources such as solar power into existing power grids presents major planning and reliability challenges. This study introduces an approach to optimize the placement of solar plants and allocation of storage in grids with high share of these variable energy sources by using a simulation framework that captures system-wide emergent behaviors. Unlike traditional engineering models focused on detailed component-level dynamics, a modified ORNL-PSERC-Alaska model based on self-organized criticality is used to reproduce the statistical features of blackouts, including cascading failures and long-range correlations. A distinctive feature of this approach is the explicit inclusion of key ingredients that shape these statistics, such as the transmission grid structure, generation and consumer buses, power flow balance, periodic dispatches, system failures, secular demand growth, demand fluctuations, and variability of renewable energy sources. When applied to the Balearic Islands grid, this method identifies generation and storage layouts that minimize storage requirements while maintaining reliability levels comparable to conventional power systems. The results offer a complementary systems-level perspective for planning resilient and efficient renewable energy integration. Full article

With increasing electrification and the connection of more renewable resources at the transmission level, bulk interconnected electric grids need to plan network expansion with new transmission facilities. The transmission expansion planning (TEP) problem is particularly challenging because of the combinatorial, integer optimization nature of the problem and the complexity of engineering analysis for any one possible solution. Network synthesis methods, that is, heuristic-based techniques for building synthetic electric grid models based on complex network properties, have been developed in recent years and have the capability of balancing multiple aspects of power system design while efficiently considering large numbers of candidate lines to add. This paper presents a methodology toward scalability in addressing the large-scale TEP problem by applying network synthesis methods. The algorithm works using a novel heuristic method, inspired by simulated annealing, which alternates probabilistic removal and targeted addition, balancing the fixed cost of transmission investment with objectives of resilience via power flow contingency robustness. The methodology is demonstrated in a test case that expands a 2000-bus interconnected synthetic test case on the footprint of Texas with new transmission to support 2025-level load and generation. Full article

The electromechanical composite transmission technology for tracked vehicles demonstrates excellent performance in energy efficiency, mobility, and ride comfort. However, due to frequent operation under harsh conditions, the components of the electric drive system, such as drive motors, are prone to failures. This paper proposes three fault-tolerant control methods for three typical fault scenarios of the electromechanical composite transmission system (ECTS) to ensure the normal operation of tracked vehicles. Firstly, an ECTS and the electromechanical coupling dynamics model of the tracked vehicle are established. Moreover, a double-layer anti-windup PID control for motors and an instantaneous optimal control strategy for the engine are proposed in the fault-free case. Secondly, an anti-windup PID control law for motors and an engine control strategy considering the state of charge (SOC) and driving demands are developed in the case of single-side drive motor failure. Thirdly, a B4 clutch control strategy during starting and a steering brake control strategy are proposed in the case of electric drive system failure. Finally, in the straight-driving condition of the tracked vehicle, the throttle opening is set as 0.6, and the motor failure is triggered at 15 s during the acceleration process. Numerical simulations verify the fault-tolerant control strategies’ feasibility, using the tracked vehicle’s maximum speed and acceleration at 30 s as indicators for dynamic performance evaluation. The simulation results show that under single-motor fault, its straight-line driving power drops by 33.37%; with electric drive failure, the drop reaches 43.86%. The vehicle can still maintain normal straight-line driving and steering under fault conditions. Full article

With the development and application of electric vehicles powered by lithium-ion batteries (LIBs) at high altitude, the lack of research on the aging laws and mechanisms of LIBs under a low-pressure aviation environment has become an important obstacle to their safe application. Herein, the influences and mechanisms of high-altitude and low-pressure environment (50 kPa) on the cycling performance of commercial pouch LIBs were systematically studied. The results showed that low air pressure caused a sharp decrease in battery capacity to 46.6% after 200 cycles, with a significant increase in charge transfer impedance by 70%, and the contribution rate of active lithium loss reached 74%. Low air pressure led to irreversible deformation of the battery, resulting in the expansion of the gap between the electrodes, poor electrolyte infiltration, and reduction of the effective lithium insertion area, which in turn induced multiple synergistic accelerated decay mechanisms, including obstructed lithium-ion transmission, reduced interfacial reaction efficiency, increased active lithium consumption, changes in heat generation structure, and a significant increase in heat generation. After applying external force, the deformation of the electrode was effectively suppressed, and the cycle capacity retention rate increased to 87.6%, which significantly alleviated the performance degradation of LIBs in low pressure environment. This work provides a key theoretical basis and engineering solutions for the design of power batteries in high-altitude areas. Full article

The YZ-ZJ DC transmission project addresses significant power transmission challenges in a specific region’s power grid, which faces unique pressures due to overlapping “growth” and “transition” periods in energy demand. This study focuses on the integration of Controllable-Line-Commutated Converters (CLCCs) into the YZ-ZJ DC transmission project at the receiving end, replacing the traditional LCCs to mitigate commutation failures during AC system faults. The main innovation lies in the development of a hybrid electromechanical–electromagnetic simulation model based on actual engineering parameters that provides a comprehensive analysis of the CLCC’s electromagnetic characteristics and system-level behavior under fault conditions. This is a significant advancement over previous research, which mainly focused on discrete electromagnetic modeling in ideal or simplified scenarios without considering the full complexity of real-world regional power grids. The research demonstrates that integrating CLCCs into the regional power grid not only prevents commutation failures but also enhances the overall reliability of the transmission system. The results show that CLCCs significantly improve fault tolerance, stabilize power transmission during faults, reduce power fluctuations in neighboring transmission lines, and enhance grid stability. Furthermore, this study confirms that the CLCC-based YZ-ZJ DC project outperforms the traditional LCC system, maintaining stable power transmission even under fault conditions. In conclusion, this study validates the feasibility of CLCCs in resisting commutation failures when integrated into a large power grid and reveals their positive impact on the regional grid. Full article

Energy resources are abundant in high-altitude regions, and the construction of power transmission and transformation projects has important value. However, harsh natural environments can increase project costs. To address the issue of insufficient accuracy caused by the impact of extreme weather factors on cost predictions for power transmission and transformation projects in high-altitude regions, this paper first constructs a four-dimensional influencing factor system covering climate and environment, engineering scale, material consumption, and technological economy. On this basis, a hybrid deep-learning model combining an improved whale optimization algorithm (IWOA) and a convolutional neural network (CNN) is then proposed. The model improves the training accuracy of CNNs and avoids falling into local optima through the use of an SGDM optimizer, the L2 regularization method, and the Bayesian optimization method. Nonlinear convergence factors and adaptive weights are introduced to enhance the WOA’s ability to optimize the CNN’s learning rate. The case analysis results show that, compared with the comparison model, the proposed IWOA-CNN model exhibits a better convergence performance and fitting effect in the training set and a better prediction effect on the test set. Its mean absolute percentage error is as low as 1.51%, which is 10.1% lower than the optimal comparison model. The root mean square error is reduced to 5.07, and the sum of squared errors is reduced by 72.4%, demonstrating high prediction accuracy. The comparative analysis of scenarios further confirms the crucial role of climate environment; that is, the prediction accuracy of models containing a climate dimension is improved by 51.6% compared to models without such a climate dimension, indicating that the nonlinear impact of low temperatures, frozen soil, and other characteristics of high-altitude regions on costs cannot be ignored. The research results of this paper enrich the method system and application scenarios for the cost prediction for power transmission and transformation projects and provide theoretical reference for engineering predictions in other complex geographical environments. Full article

This review systematically examines recent advances in self-powered wireless sensing technologies based on triboelectric nanogenerators (TENGs), focusing on innovative methods that leverage breakdown discharge effects to achieve high-precision and long-distance signal transmission. These methods offer novel technical pathways and theoretical frameworks for next-generation wireless sensing systems. To address the core limitations of conventional wireless sensors, such as a restricted transmission range, high power consumption, and suboptimal integration, this analysis elucidates the mechanism of the generation of high-frequency electromagnetic waves through localized electric field ionization induced by breakdown discharge. Key research directions are synthesized to enhance TENG-based sensing capabilities, including novel device architectures, the optimization of RLC circuit models, the integration of machine learning algorithms, and power management strategies. While current breakdown discharge sensors face challenges such as energy dissipation, multimodal coupling complexity, and signal interpretation barriers, future breakthroughs in material engineering and structural design are anticipated to drive advancements in efficiency, miniaturization, and intelligent functionality in this field. Full article

Microgrids are increasingly being adopted as alternatives to traditional power transmission networks, necessitating improved performance strategies. Various mathematical optimization techniques are used to determine optimal controller parameters for these systems. These optimization methods can generally be categorized into natural, biological, and engineering-based approaches. In this research, the authors evaluated and compared several optimization techniques to enhance the secondary controller of DC microgrids, focusing on reducing operating time and minimizing error rates. Optimization tools were utilized to identify the optimal gain control parameters, aiming to achieve the best possible system performance. The enhanced controller response enables quicker recovery to steady-state conditions during sudden disturbances. The root-mean-square error (RMSE) served as a performance metric, with the proposed approach achieving a 15% reduction in RMSE compared to previous models. This improvement contributes to faster response times and lower energy consumption in microgrid operation. Full article

In a smart microgrid (SMG) system that deals with unpredictable loads and incorporates fluctuating solar and wind energy, it is crucial to have an efficient method for controlling frequency in order to balance the power between generation and load. In the last decade, cyberattacks have become a growing menace, and SMG systems are commonly targeted by such attacks. This study proposes a framework for the frequency management of an SMG system using an innovative combination of a smart controller (i.e., the Fuzzy Logic Controller (FLC)) with three conventional cascaded controllers, including Fractional-Order PI (FOPI), Tilt Integral Fractional Derivative (TID^μ), and Proportional Integral Derivative Acceleration (PIDA). The recently released Eel and Grouper Optimization (EGO) algorithm is used to fine-tune the parameters of the proposed controller. This algorithm was inspired by how eels and groupers work together and find food in marine ecosystems. The Integral Time Squared Error (ITSE) of the frequency fluctuation (ΔF) around the nominal value is used as an objective function for the optimization process. A diesel engine generator (DEG), renewable sources such as wind turbine generators (WTGs), solar photovoltaics (PVs), and storage components such as flywheel energy storage systems (FESSs) and battery energy storage systems (BESSs) are all included in the SMG system. Additionally, electric vehicles (EVs) are also installed. In the beginning, the supremacy of the adopted EGO over the Gradient-Based Optimizer (GBO) and the Smell Agent Optimizer (SAO) can be witnessed by taking into consideration the optimization process of the recommended regulator’s parameters, in addition to the optimum design of the membership functions of the fuzzy logic controller by each of these distinct algorithms. The subsequent phase showcases the superiority of the proposed EGO-based FFOPI-TID^μ-PIDA structure compared to EGO-based conventional structures like PID and EGO-based intelligent structures such as Fuzzy PID (FPID) and Fuzzy PD-(1 + PI) (FPD-(1 + PI)); this is across diverse symmetry operating conditions and in the presence of various cyberattacks that result in a denial of service (DoS) and signal transmission delays. Based on the simulation results from the MATLAB/Simulink R2024b environment, the presented control methodology improves the dynamics of the SMG system by about 99.6% when compared to the other three control methodologies. The fitness function dropped to 0.00069 for the FFOPI-TID^μ-PIDA controller, which is about 200 times lower than the other controllers that were compared. Full article

Recent advances in sensor technology, data acquisition, and signal processing have enabled the development of data-driven structural health monitoring (SHM) strategies, offering a powerful alternative or complement to traditional model-based approaches. These approaches rely on damage-sensitive features (DSFs) extracted from vibration measurements. This study introduces an innovative, unsupervised learning framework leveraging transmissibility functions (TFs) as DSFs due to their local sensitivity to changes in dynamic behavior and their ability to operate without requiring input excitation measurements—an advantage in civil engineering applications where such data are often difficult to obtain. The novelty lies in the use of sequential sensor pairings based on structural connectivity to construct TFs that maximize damage sensitivity, combined with one-class classification algorithms for automatic damage detection and a damage index for spatial localization within sensor resolution. The method is evaluated through numerical simulations with noise-contaminated data and experimental tests on a masonry arch bridge model subjected to progressive damage. The numerical study shows detection accuracy above 90% with one-class support vector machine (OCSVM) and correct localization across all damage scenarios. Experimental findings further confirm the proposed approach’s localization capability, especially as damage severity increases, aligning well with observed damage progression. These results demonstrate the method’s practical potential for real-world SHM applications. Full article

Apparent power and power factor are crucial metrics for evaluating the energy transmission efficiency and reactive power management in power systems. The increasing complexity of power load structures, driven by evolving energy production and consumption models, has intensified the nonlinear and unbalanced characteristics of circuits, presenting significant challenges to accurate apparent power measurement. The IEEE 1459-2010 standard introduces the concept of effective apparent power to enhance the assessment of energy transmission efficiency under non-sinusoidal and unbalanced conditions. However, the absence of a physical standard and a standardized traceability method for effective apparent power results in inconsistent measurement outcomes across instruments. This study proposes a novel method to trace effective apparent power measurements to the International System of Units (SI) benchmarks, based on the loss characteristics of transmission lines. The method includes a comprehensive analysis of measurement uncertainty. Simulation and experimental validation confirm that the proposed traceability circuit can achieve a measurement uncertainty of 0.0110% (coverage factor k = 2), satisfying the engineering requirement of expanded uncertainty U approximately 0.02% (k = 2). These results demonstrate the method’s practical suitability for engineering applications. Full article