Search Results (234)

The worldwide air pollution situation reveals significant environmental challenges. In addition to being a major contributor to the deterioration of air quality, particulate matter (PM) is also an important factor affecting the performance of solar energy systems given its ability to decrease light transmission to solar panels. As part of our research, the present investigation involves monitoring concentrations of PM using a high-performance optical instrument, the in situ calibration protocol of which is described in detail. For the city of Rabat, observations revealed significant variations in concentrations between day and night, with peaks observed around 8 p.m. correlating with high relative humidity and low wind speeds, and the highest levels recorded in February with a monthly average value reaching 75 µm/m³. In addition, an experimental protocol was set up for an analysis of the elemental composition of particles in the same city using SEM/EDS, providing a better understanding of their morphology. To assess the impact of meteorological variables on PM concentrations in two distinct climatic environments, a database from the city of Marrakech for the year 2024 was utilized. Overall, the distribution of PM values during this period did not fluctuate significantly, with a monthly average value not exceeding 45 µm/m³. The random forest method identified the most influential variables on these concentrations, highlighting the strong influence of the type of environment. The findings provide crucial information for the modeling of solar installations’ soiling and for improving understanding of local air quality. Full article

Dynamic Line Rating (DLR) technology is presented as a key solution to optimize the transmission capacity of power lines without the need to make investments in new infrastructure. Unlike traditional methods based on static estimates, DLR allows the thermal capacity of conductors to be evaluated in real time, considering the environmental and operational conditions. This article presents a state-of-the-art analysis of this technology, including a review of the main solutions currently available on the market. Likewise, the influence of variables such as ambient temperature, wind speed and direction or solar radiation in the determination of dynamic load capacity is discussed. It also reviews various pilot and commercial projects implemented internationally, evaluating their results and lessons learned. Finally, the main technological, regulatory, and operational challenges faced by the mass adoption of DLR are identified, including aspects such as the prediction of the dynamic capacity value, combination with other flexibility options, or integration with network management systems. This review is intended to serve as a basis for future developments and research in the field. Full article

Efficient prediction of shallow-water acoustic transmission loss (TL) is crucial for underwater detection, recognition, and communication systems. Traditional physical modeling methods require repeated calculations for each new scenario in practical waveguide environments, leading to low computational efficiency. Deep learning approaches, based on data-driven principles, enable accurate input–output approximation and batch processing of large-scale datasets, significantly reducing computation time and cost. To establish a rapid prediction model mapping sound speed profiles (SSPs) to acoustic TL through controllable generation, this study proposes a hybrid framework that integrates a variational autoencoder (VAE) and a normalizing flow (Flow) through a two-stage training strategy. The VAE network is employed to learn latent representations of TL data on a low-dimensional manifold, while the Flow network is additionally used to establish a bijective mapping between the latent variables and underwater physical parameters, thereby enhancing the controllability of the generation process. Combining the trained normalizing flow with the VAE decoder could establish an end-to-end mapping from SSPs to TL. The results demonstrated that the VAE–Flow network achieved higher computational efficiency, with a computation time of 4 s for generating 1000 acoustic TL samples, versus the over 500 s required by the KRAKEN model, while preserving accuracy, with median structural similarity index measure (SSIM) values over 0.90. Full article

Reliable wireless communication is essential for mobile robotic systems operating in dynamic environments, particularly in the context of smart mobility and cloud-integrated urban infrastructures. This article presents an experimental study analyzing the impact of robot motion dynamics on wireless network performance, contributing to the broader discussion on data reliability and communication efficiency in intelligent transportation systems. Measurements were conducted using a quadruped robot equipped with an onboard edge computing device, navigating predefined trajectories in a laboratory setting designed to emulate real-world variability. Key wireless parameters, including signal strength (RSSI), latency, and packet loss, were continuously monitored alongside robot kinematic data such as speed, orientation (roll, pitch, yaw), and movement patterns. The results show a significant correlation between dynamic motion—especially high forward velocities and rotational maneuvers—and degradations in network performance. Increased robot speeds and frequent orientation changes were associated with elevated latency and greater packet loss, while static or low-motion periods exhibited more stable communication. These findings highlight critical challenges for real-time data transmission in mobile IoRT (Internet of Robotic Things) systems, and emphasize the role of network-aware robotic behavior, interoperable communication protocols, and edge-to-cloud data integration in ensuring robust wireless performance within smart city environments. Full article

Single-phase short-circuit faults are severe asymmetrical fault modes in high renewable energy power systems. They can easily cause large-scale renewable energy to enter the low-voltage ride-through (LVRT) state. When such symmetrical or asymmetrical faults occur in the transmission channels of high-proportion wind power clusters, they may trigger the tripping of thermal power units and a transient voltage drop in most wind turbines in the high-proportion wind power area. This causes an instantaneous active power deficiency and poses a low-frequency oscillation risk. To address the deficiencies of wind turbine units in fault ride-through (FRT) and active frequency regulation capabilities, a power emergency support scheme for wind power clusters based on doubly fed variable-speed pumped storage dynamic excitation is proposed. A dual-channel energy control model for variable-speed pumped storage units is established via AC excitation control. This model provides inertia support and FRT energy simultaneously through AC excitation control of variable-speed pumped storage units. Considering the transient stability of the power network in the wind power cluster transmission system, this scheme prioritizes offering dynamic reactive power to support voltage recovery and suppresses power oscillations caused by power deficiency during LVRT. The electromagnetic torque completed the power regulation within 0.4 s. Finally, the effectiveness of the proposed strategy is verified through modeling and analysis based on the actual power network of a certain region in Northeast China. Full article

Aiming at the problem of the insufficient stability of the unified model of steering angle transmission ratio at high speeds, we introduce a novel control strategy that integrates the yaw rate gain, lateral acceleration gain, vehicle speed and steering wheel angle, achieving great improvements in a simulation. The new control strategy uses a genetic algorithm to optimize the yaw rate and lateral acceleration gain values at different speeds, and the two are weighted. The ideal variable-angle transmission ratio control strategy is designed by using the unified model of steering angle transmission ratio at different speed intervals. The simulation results show that the strategy reduces the steering wheel angle peak by 67.12% compared with the fixed-angle transmission at low speeds. Compared with the unified model of steering angle transmission ratio at high speeds, the peak values of the yaw rate, the lateral acceleration and sideslip angle of the vehicle are reduced by 7%, 5.67% and 11.67%, respectively, which effectively improves the steering stability of the vehicle. Full article

Integrating the electric motor with a multi-speed transmission is an effective way to improve the efficiency and performance of battery electric vehicles (BEVs). This paper innovatively proposes a design method for matching a single-motor and dual-speed dual-clutch transmission (2-Speed Wet DCT) powertrain system and constructs a variable speed efficiency model (VSEM) and constant speed efficiency model (CSEM) for the inverter, motor, and transmission. Research shows that the design parameters of the motor and transmission significantly affect the optimal powertrain system. This study uses an enhanced NSGA-II multi-objective genetic algorithm to optimize the driving performance of energy efficiency and powertrain cost under two different acceleration times (10 s and 12 s), with the key parameters of the motor and transmission as optimization variables and dynamic indicators as constraints, and compares VSEM and CSEM. The optimization results indicate that VSEM have better energy-saving effects than CSEM, with the energy consumption reduced by 3.7% and 3.3% under the two driving performances, respectively. The Pareto frontier further confirms that, for multi-speed transmission systems in electric vehicles, matching a high-power, high-torque motor with a smaller transmission ratio powertrain can achieve higher energy efficiency and thus longer driving range. Additionally, this study quantifies the correlation between energy efficiency and powertrain cost using grey relational analysis (GRA), with a result of 0.77431. Full article

Renewable energy sources (RESs) are increasingly combined into the power system due to market liberalization and environmental and economic benefits, but their weather-dependent variability causes power production and demand mismatches, leading to issues like frequency and regional power transmission fluctuations. To maintain synchronization in power systems, frequency must remain constant; disruptions in the proper balance of production and load might produce frequency variations, risking serious issues. Therefore, a mechanism known as load frequency control (LFC) or automated generation control (AGC) is needed to keep the frequency and tie-line power within predefined stable limits. In this study, advanced proportional–integral–derivative PID controllers such as fractional-order PID (FOPID), cascaded PI(PDN), and PI(1+DD) for LFC in a two-area power system integrated with RES are optimized using the catch fish optimization algorithm (CFA). The controllers’ optimal gains are attained through using the integral absolute error (IAE) and ITAE objective functions. The performance of LFC with CFA-tuned PID, PI, cascaded PI(PDN), and FOPID, PI(1+DD) controllers is compared to other optimization techniques, including sine cosine algorithm (SCA), particle swarm optimization (PSO), brown bear algorithm (BBA), and grey wolf optimization (GWO), in a two-area power system combined with RESs under various conditions. Additionally, by contrasting the performance of the PID, PI, cascaded PI(PDN), and FOPID, PI(1+DD) controllers, the efficiency of the CFA is confirmed. Additionally, a sensitivity analysis that considers simultaneous modifications of the frequency bias coefficient (B) and speed regulation (R) within a range of ±25% validates the efficacy and dependability of the suggested CFA-tuned PI(1+DD). In the complex dynamics of a two-area interconnected power system, the results show how robust the suggested CFA-tuned PI(1+DD) control strategy is and how well it can stabilize variations in load frequency and tie-line power with a noticeably shorter settling time. Finally, the results of the simulation show that CFA performs better than the GWO, BBA, SCA, and PSO strategies. Full article

This paper presents a comparison of some different configurations of electro-hydrostatic actuators (EHA). The gear pump EHA has a simpler mechanical configuration, but the electronic power command circuits and the electric motor are in high demand due to the very frequent speed variations. The variable piston pump EHA has a more complicated mechanical configuration, but the electronic power command circuits and the main electric motor are less loaded due to the constant speed of the electric motor. The variable displacement pump control can be made either using an electric motor and mechanical transmission, or an additional hydraulic circuit, to modify the swash plate angle. In total, four EHA configurations are studied in this paper (one with a gear pump and three with variable axial piston pumps). The paper aims to advantages and disadvantages of each type of EHA, using numerical simulations. Full article

Hydrostatic transmissions are essential in applications demanding variable torque and speed, such as mining and agricultural machinery, due to their compact design, high power-to-weight ratio, and efficient variable speed control. Despite these advantages, their inherent nonlinearities and susceptibility to parametric uncertainties pose significant challenges for precise motion control. This study presents a comparative analysis of classical PID and robust H-infinity controllers for regulating the speed of hydraulic motors under varying torsional loads. A linearized uncertain system model is developed using upper Linear Fractional Transformations (LFTs) to capture key parametric uncertainties. A simplified H-infinity controller is designed to robustly manage system dynamics, particularly addressing phase lags induced by uncertain loads. Simulation results demonstrate that the H-infinity controller offers superior performance over the PID controller in terms of stability, disturbance rejection, and robustness to load fluctuations. This work contributes a practically viable robust control solution for improving the reliability and precision of electro-hydraulic systems, particularly in demanding, real-world environments. Full article

The working of the Internet of Things (IoT) ecosystem indeed depends extensively on the mechanisms of real-time data collection, sharing, and automatic operation. Among these fundamentals, wireless sensor networks (WSNs) are important for maintaining a countenance with their many distributed Sensor Nodes (SNs), which can sense and transmit environmental data wirelessly. Because WSNs possess advantages for remote data collection, they are severely hampered by constraints imposed by the limited energy capacity of SNs; hence, energy-efficient routing is a pertinent challenge. Therefore, in the case of clustering and routing mechanisms, these two play important roles where clustering is performed to reduce energy consumption and prolong the lifetime of the network, while routing refers to the actual paths for transmission of data. Addressing the limitations witnessed in the conventional IoT-based routing of data, this proposal presents an FL-oriented framework that presents a new energy-efficient routing scheme. Such routing is facilitated by the ADDQL model, which creates smart high-speed routing across changing scenarios in WSNs. The proposed ADDQL-IRHO model has been compared to other existing state-of-the-art algorithms according to multiple performance metrics such as energy consumption, communication delay, temporal complexity, data sum rate, message overhead, and scalability, with extensive experimental evaluation reporting superior performance. This also substantiates the applicability and competitiveness of the framework in variable-serviced IoT-oriented WSNs for next-gen intelligent routing solutions. Full article

As a core transmission component of modern industrial equipment, the operation status of the gearbox has a significant impact on the reliability and service life of major machinery. In this paper, we propose an intelligent diagnosis framework based on Empirical Mode Decomposition and multimodal feature co-optimization and innovatively construct a fault diagnosis model by fusing a multi-scale convolutional neural network and a lightweight convolutional attention model. The framework extracts the multi-band features of vibration signals through the improved multi-scale convolutional neural network, which significantly enhances adaptability to complex working conditions (variable rotational speed, strong noise); at the same time, the lightweight convolutional attention mechanism is used to replace the multi-attention of the traditional Transformer, which greatly reduces computational complexity while guaranteeing accuracy and realizes highly efficient, lightweight local–global feature modeling. The lightweight convolutional attention is adaptively captured by the dynamic convolutional kernel generation strategy to adaptively capture local features in the time domain, and combined with grouped convolution to enhance the computational efficiency further; in addition, parameterized revised linear units are introduced to retain fault-sensitive negative information, which enhances the model’s ability to detect weak faults. The experimental findings demonstrate that the proposed model achieves an accuracy greater than 98.9%, highlighting its exceptional diagnostic accuracy and robustness. Moreover, compared to other fault diagnosis methods, the model exhibits superior performance under complex working conditions. Full article

The electrolytic capacitor-less PMSM drive system presents complex nonlinear characteristics. Since electrolytic capacitor-less systems exhibit low inertia due to the absence of energy storage components, traditional controllers struggle to achieve the dynamic optimization of phase and amplitude margins, resulting in power transmission mismatches that trigger DC bus voltage surges. This severely limits the dynamic response capability and reliable operation of the system across full operating conditions, leading to an insufficient wide-speed-range performance and disturbance rejection. This study investigates the stable operation mechanism under intermittent working conditions by analyzing DC bus voltage transient characteristics. It optimizes control parameters for stable intermittent operations and establishes a neural network-based adaptive controller model. By modeling the correlation between hardware parameters and control parameters in drive systems under frequent start–stop conditions, this research achieves dynamic controllability of the controller during intermittent operations. This approach enhances the computational accuracy of the drive system control model, ultimately improving system-wide operational reliability and adaptability. Experimental validation confirms the effectiveness of this approach, showing significant reliability improvements in capacitor-less variable-frequency speed-control systems. Key innovations include: (1) BP neural network integration for dynamic parameter optimization, (2) impulse voltage suppression through adaptive control matching, and (3) enhanced transient response via machine learning-enhanced speed regulation. The test results demonstrate a 63% reduction in bus voltage fluctuations and 35% improvement in load transition responses compared to conventional PID-based systems, proving the strategy’s practical viability for industrial drive applications. Full article

Binary logic transmission (BLT), a stepped transmission system, has been utilized in military vehicles and heavy-duty commercial vehicles due to its high transmission efficiency, strong load-bearing capacity, and compact structure. Its adaptability to agricultural tractor operations is notable. This study modularizes BLT into a binary logic transmission unit (BLT-U) for application in agricultural tractor Hydro-Mechanical Continuously Variable Transmission (HMCVT), optimizing its wet clutch to enhance HMCVT shifting performance. This provides a basis for BLT-U’s application in other transmission systems and subsequent optimization. A wet clutch test bench was employed to validate the modeling approach. The optimal BLT-U configuration was selected using both light/heavy load conditions and subjective–objective evaluation criteria. The WOA improved the spread value in the GRNN algorithm, establishing a GRNN to predict the optimal range for wet clutch design values in BLT-U; the model validation showed an average correlation coefficient of 0.92 for speed curves and an average relative error of 5.58% for dynamic loads. Under light-load conditions, the optimal configuration improved average and maximum scores by 13.38% and 11.53%, respectively, while under heavy-load conditions, the corresponding improvements were 9.38% and 5.86%. Under light-load conditions, the optimized GRNN reduced total relative error by 39.6%, while under heavy-load conditions, it achieved a 61% reduction. This study confirms the rationality of the modeling method, identifies Configuration 1 as optimal, and determines the optimal range for clutch design values under light-load and heavy-load conditions, respectively. Full article

Eliminating fossil fuel as early as possible and electrifying everything by green sustainable electric power are some of the primary solutions for tackling the climate emergency. Solar electricity generated by photovoltaics is now the cheapest source of electric power generation. The cost of electrochemical storage batteries is plummeting and the combination of photovoltaics and batteries at a utility scale can provide a lower cost than electrical power generated by fossil fuel in many parts of the world. Thus, today, we nearly have a solution in sustainable green electrical power generation and storage. Globally, we have adopted alternating-current electric power infrastructure over direct-current power due to the invention of the transformer. However, due to the advancements in power electronics and loads based on semiconductors, the situation is totally different today. Except for induction motors running at rated speed, all loads using variable-frequency drive are direct-current loads. Photovoltaics, batteries, and virtually all loads are based on direct-current power. Considering power generation, transmission, distribution, and utilization as a single entity, we are wasting a large amount of power by using our alternating-current power electricity infrastructure. By using end-to-end direct-current power networks, we can save energy and capital investment in electricity infrastructure as well as the cost of the loads, as compared to the existing power infrastructure. Based on an end-to-end direct-current power network, a new concept for community solar is proposed in this paper. Without connecting to the existing grid, community solar can provide 24 × 7 electric power to residents. The proposed infrastructure concept can also have a transformative role in areas which are providing very high growth of green electric power. This concept can have an immediate profound impact on any new constructions in most parts of the world. Full article