Search Results (168)

In this paper, the dynamics of long payload transportation via two bridge cranes in three dimensions is studied. The position of the cranes is known, and it is used as a kinematic coercion. Using the independent variables, equations of motion are derived. Two types of crane motion are considered: the Cubic Spline Trajectory (CST) and the trapezoidal one. Numerical simulations are compared with experimental results from the literature on the long payload moving in one plane. Qualitatively similar results were obtained. The anti-swing control is verified and compared with each function of the motion. The difference between the distance of the cranes’ bridge and payload’s anchorage length is considered. It does not reveal any relevant improvement of the anti-swing cargo control. The considered model can be used in the anti-swing wind excitation control system in future work. Also, this work can be used to investigate the energy harvesting system in long payload transportation. Full article

A new energy storage unit, which is fed by a piezoelectric wind energy harvester, is explored. The outputs of a three-phase piezoelectric wind energy device have been initially recorded from the laboratory experiments. Following the records of voltage outputs, the power ranges of the device were measured at several hundred microwatts. The main issue of piezoelectric voltage generation is that voltage waveforms of piezoelectric materials have high total harmonic distortion (THD) with incredibly high subharmonics and superharmonics. Therefore, such a material reply causes a certain power loss at the output of the wind energy generator. In order to fix this problem, we propose a combination of a rectifier and a storage system, where they can operate compatibly under high THD rates (i.e., 125%). Due to high THD values, current–voltage characteristics are not linear-dependent; indeed, because of capacitive effect of the piezoelectric (i.e., lead zirconium titanite) material, harvested power from the material is reduced by nearly a factor of 20% in the output. That also negatively affects the storage on the Li-based battery. In order to compensate, the output waveform of the device, the waveforms, which are received from the energy-harvester device, are first rectified by a full-wave rectifier that has a maximum power point tracking (MPPT) unit. The SOC values prove that almost 40% of the charge is stored in 1.2 s under moderate wind speeds, such as 6.1 m/s. To conclude, a better harvesting performance has been obtained by storing the energy into the Li-ion battery under a current–voltage-controlled boost converter technique. Full article

Traditional energy harvesting systems, such as photovoltaics and wind power, often rely on external environmental conditions and are typically associated with contact-based vibration wear and bulky structures. This study introduces light-fueled self-vibration to propose a self-harvesting system, consisting of liquid crystal elastomer fibers, two resistors, and two piezoelectric cantilever beams arranged symmetrically. Based on the photothermal temperature evolution, we derive the governing equations of the liquid crystal elastomer fiber–piezoelectric beam system. Two distinct states, namely a self-harvesting state and a static state, are revealed through numerical simulations. The self-oscillation results from light-induced cyclic contraction of the liquid crystal elastomer fibers, driving beam bending, stress generation in the piezoelectric layer, and voltage output. Additionally, the effects of various system parameters on amplitude, frequency, voltage, and power are analyzed in detail. Unlike traditional vibration energy harvesters, this light-fueled self-harvesting system features a compact structure, flexible installation, and ensures continuous and stable energy output. Furthermore, by coupling the light-responsive LCE fibers with piezoelectric transduction, the system provides a non-contact actuation mechanism that enhances durability and broadens potential application scenarios. Full article

The demand for sustainable energy generation and storage methods has become inevitable. As a result, numerous sectors are investing in research focused on energy harvesting (EH) techniques. In this context, a promising area involves integrating piezoelectric materials into unmanned aerial vehicles (UAVs)—an application that enables electrical energy generation from the kinetic energies produced during flight. This article aims to use polyvinylidene fluoride (PVDF) piezoelectric transducers coupled to an EH power management unit (LTC3588-1) to convert and store electrical energy generated by wind from the propellers and motor vibration. Methodologically, the motor and transducers are characterized, a model is developed using LTSpice^®, and experimental validation of the performance of this coupling is carried out for output voltages (V_out) of 1.8 V, 2.5 V, 3.3 V, and 3.6 V. With a motor rotation speed of 3975 rpm, the transducers generated a voltage amplitude of 17.3 V, enabling the capacitor coupled to the EH power management unit—adjusted to the highest V_out—to be charged in approximately 162 s. Thus, this study demonstrated the feasibility of using PVDF as a piezoelectric nanogenerator in UAVs, enabling onboard electronic circuits and sensors to be powered while reserving the battery solely for propulsion, thereby increasing flight autonomy. Full article

Energy harvesting (EH) is a rapidly evolving domain that is primarily focused on capturing and converting ambient energy sources into more convenient and usable forms. These sources, which range from traditional renewable sources such as solar or wind power to thermal gradients and vibrations, present an alternative to typical power generation. The temptation to use energy harvesting systems is in their potential to power low-power devices, such as environment monitoring devices, without relying on conventional power grids or standard battery implementations. This improves the sustainability and self-sufficiency of IoT devices and reduces the environmental impact of conventional power systems. Applications of EH include wearable health monitors, wireless sensor networks, and remote structural sensors, where frequent battery replacement is impractical. However, these systems also face challenges such as intermittent energy availability, limited storage capacity, and low power density, which require innovative design approaches and efficient energy management. The paper provides a general overview of the subsystems present in the energy harvesting systems and a comprehensive overview of the energy transducer technologies used in energy harvesting systems. Full article

The electric power equipment industry is rapidly advancing toward “informationization,” with the swift progression of intelligent sensing technology serving as a key driving force behind this transformation, thereby triggering significant changes in global electric power equipment. In this process, intelligent sensing has created an urgent demand for high-performance integrated power systems that feature compact size, lightweight design, long operational life, high reliability, high energy density, and low cost. However, the performance metrics of traditional power supplies have increasingly failed to meet the requirements of modern intelligent sensing, thereby significantly hindering the advancement of intelligent power equipment. Energy harvesting technology, characterized by its long operational lifespan, compact size, environmental sustainability, and self-sufficient operation, is capable of capturing renewable energy from ambient power sources and converting it into electrical energy to supply power to sensors. Due to these advantages, it has garnered significant attention in the field of power sensing. This paper presents a comprehensive review of the current state of development of energy harvesting technologies within the power environment. It outlines recent advancements in magnetic field energy harvesting, electric field energy harvesting, vibration energy harvesting, wind energy harvesting, and solar energy harvesting. Furthermore, it explores the integration of multiple physical mechanisms and hybrid energy sources aimed at enhancing self-powered applications in this domain. A comparative analysis of the advantages and limitations associated with each technology is also provided. Additionally, the paper discusses potential future directions for the development of energy harvesting technologies in the power environment. Full article

With the rapid growth of the Internet of Things, intelligent agriculture is becoming increasingly important. Traditional agricultural monitoring methods, which rely on fossil fuels and complex wiring, hinder progress. This work introduces a hybrid nanogenerator based on a rotational-swinging mechanism (RSM-HNG) that combines triboelectric nanogenerators (TENGs) and electromagnetic generators (EMGs) for efficient wind energy harvesting and smart agriculture monitoring. The parallelogram mechanism and motion conversion structure enable the stacking and simultaneous contact-separation of multiple TENG layers. Moreover, it allows the TENG and EMG units to operate simultaneously, which improves energy harvesting efficiency and extends the system’s lifespan compared to traditional disc-based friction wind energy harvesting methods. With four stacked layers, the short-circuit current of the TENG increases from 16 μA to 40 μA, while the transferred charge rises from 0.3 μC to 1.5 μC. By optimizing the crank angle, material selection, and substrate structure, the output performance of the RSM-HNG has been significantly enhanced. This technology powers a self-sustaining wireless monitoring system for temperature, humidity, an electronic clock, and road guidance. The RSM-HNG provides continuous energy for smart agriculture, animal husbandry, and environmental monitoring, all driven by wind energy. It holds great potential for regions with abundant wind resources but limited electricity access, offering valuable applications in these areas. Full article

With the large-scale global deployment of photovoltaics (PV), traditional monitoring technologies face challenges such as wiring difficulties, high energy consumption, and high maintenance costs in remote or complex terrains, which limit long-term environmental sensing. Therefore, energy-harvesting systems are crucial for the intelligent operation of photovoltaic systems; however, their deployment depends on the accurate mapping of wind energy fields and solar irradiance fields. This study proposes a multi-scale simulation method based on computational fluid dynamics (CFD) to optimize the placement of energy-harvesting systems in photovoltaic power plants. By integrating wind and irradiance distribution analysis, the spatial characteristics of airflow and solar radiation are mapped to identify high-efficiency zones for energy harvesting. The results indicate that the top of the photovoltaic panel exhibits a higher wind speed and reflected irradiance, providing the optimal location for an energy-harvesting system. The proposed layout strategy improves overall energy capture efficiency, enhances sensor deployment effectiveness, and supports intelligent, maintenance-free monitoring systems. This research not only provides theoretical guidance for the design of energy-harvesting systems in PV stations but also offers a scalable method applicable to various geographic scenarios, contributing to the advancement of smart and self-powered energy systems. Full article

This study presents the experimental results of an energy harvesting system comprising a cylindrical bluff body coupled with a cantilever beam. A piezoelectric sensor was installed on the beam to generate electrical voltage during the object’s vibrations at the beam’s free end. The research aimed to evaluate the impact of the bluff body’s mass and diameter on the efficiency of the piezoelectric energy harvesting system. Vibrations of the test object were induced by airflow within a chamber of a closed-loop wind tunnel. Five different bluff body masses were analyzed for each of three cylindrical diameters across an airflow velocity range of 1 m/s to 10 m/s. These experiments allowed for the recording of a series of voltage signals over time. The signals were then subjected to Fast Fourier Transform (FFT) analysis. Subsequently, the relationship between vibration frequency and airflow velocity was examined. The peak-to-peak voltage value was also analyzed to provide an overall assessment of the energy harvesting efficiency of the system under investigation. Finally, the 0–1 test for chaos was additionally employed as a diagnostic tool to assess the complexity of system dynamics based on time series data. This test allowed for distinguishing between oscillatory behavior and cases where the system became trapped in a potential well, revealing key transitions in dynamic regimes. Full article

This study introduces an innovative set of guide vanes that increase the efficiency of Vertical Axis Wind Turbines (VAWT) using winds generated by vehicles traveling on highways. The increase in efficiency is based on enhancing the airflow interaction as the vehicle moves past the turbine. Initial Computational Fluid Dynamics (CFD) simulations with two guide vanes setups demonstrated a 56.81% increase in power output under wind generated by passenger vehicles. Further design enhancements, incorporating three guide vanes with optimized geometries, led to a 242% improvement in power generation. Additional simulations evaluated the performance under wind conditions generated by larger vehicles, such as buses. The three guide vanes configuration yielded a 102% increase in energy capture efficiency in these scenarios. The findings suggest that vehicle-induced winds—typically an untapped energy source—can be effectively harvested using tailored turbine system designs. By integrating passive flow control strategies such as guide vanes, VAWTs can operate more efficiently in highway environments. This research highlights a novel pathway for enhancing renewable energy systems and supports broader efforts toward sustainable energy development through the utilization of unconventional wind sources. This performance enhancement is primarily due to the aerodynamic redirection of airflow toward the advancing blade and away from the returning blade, reducing drag and improving torque generation. Full article

Urban distributed energy systems play a crucial role in the development of sustainable and low-carbon cities. Evaluating urban wind resources is essential for effective wind energy harvesting, which requires detailed information about the urban flow field. Computational fluid dynamics (CFD) has emerged as a viable and scalable method for assessing urban wind resources. This review paper synthesizes the characteristics of the urban wind environment and resources, outlines the general framework for CFD-aided wind resource assessment, and addresses future challenges and perspectives. It highlights the critical need to optimize wind energy harvesting in complex built environments. The paper discusses the conditions for urban wind resource assessment, particularly the extraction of boundary conditions and the performance of small wind turbines (SWTs). Additionally, it notes that while large eddy simulation (LES) is a high-fidelity model, it is still less commonly used compared to Reynolds-averaged Navier–Stokes (RANS) models. Several challenges remain, including the broader adoption of high-fidelity LES models, the integration of wake models and extreme conditions, and the application of these methods at larger scales in real urban environments. The potential of multi-scale modeling approaches to enhance the feasibility and scalability of these methods is also emphasized. The findings are intended to promote the utilization and further development of CFD methods to accelerate the creation of resilient and energy-efficient cities, as well as to foster interdisciplinary innovation in wind energy systems. Full article

An airborne wind energy system (AWES) harvests wind at a higher altitude above conventional wind turbines using tethered flying devices. For the design and development of an AWES, we need to know the representative wind speed profile, and its temporal variation is also quite important for the optimization of operation control. This study investigates wind speed profiles up to 3000 m, utilizing ERA5 data spanning from 2000 to 2022 and measured data from a laser wind radar. The long-term averaged wind profile is statistically analyzed, as well as wind profiles with different cumulative probabilities, which are generally consistent with the logarithmic law. Statistical results show that the frequency of negative shear is more than 85% in instantaneous wind profiles, with a greater likelihood at altitudes between 500 m and 1500 m. Fluctuations in wind speed and direction based on 10 min averaged wind speed data have also been provided, which are described by a normal distribution. The wind speed fluctuations primarily concentrate within 2 m/s, with a standard deviation of approximately 0.45 m/s. The wind direction fluctuations are severe at the ground layer and show a rapid decay trend with increasing altitude and averaged wind speed. These results can support the design and control optimization of the AWES. Full article

Energy harvesting in the automotive sector is a rapidly growing field aimed at improving vehicle efficiency and sustainability by recovering wasted energy. Various technologies have been developed to convert mechanical, thermal, and environmental energy into electrical power, reducing dependency on traditional energy sources. This manuscript provides a comprehensive review of energy harvesting applications/methodologies, aiming to trace the research lines and future developments. This work identifies the main categories of harvesting solutions, namely mechanical, thermal, and hybrid/environmental solar–wind systems; each section includes a detailed review of the technical and scientific state of the art and a comparative analysis with detailed tables, allowing the state of the art to be mapped for identification of the strengths of each solution, as well as the challenges and future developments needed to enhance the technological level. These improvements focus on energy conversion efficiency, material innovation, vehicle integration, energy savings, and environmental sustainability. The mechanical harvesting section focuses on energy recovery from vehicle vibrations, with emphasis on regenerative suspensions and piezoelectric-based solutions. Specifically, solutions applied to suspensions with electric generators can achieve power outputs of around 1 kW, while piezoelectric-based suspension systems can generate up to tens of watts. The thermal harvesting section, instead, explores methods for converting waste heat from an internal combustion engine (ICE) into electrical power, including thermoelectric generators (TEGs) and organic Rankine cycle systems (ORC). Notably, ICEs with TEGs can recover above 1 kW of power, while ICE-based ORC systems can generate tens of watts. On the other hand, TEGs integrated into braking systems can harvest a few watts of power. Then, hybrid solutions are discussed, focusing on integrated mechanical and thermal energy recovery systems, as well as solar and wind energy harvesting. Hybrid solutions can achieve power outputs above 1 kW, with the main contribution from TEGs (≈1 kW), compared to piezoelectric systems (hundreds of W). Lastly, a section on commercial solutions highlights how current scientific research meets the automotive sector’s needs, providing significant insights for future development. For these reasons, the research results aim to be guidelines for a better understanding of where future studies should focus to improve the technological level and efficiency of energy harvesting solutions in the automotive sector. Full article

The increasing demand for self-powered sensors and wireless sensor networks, particularly for environmental and structural health monitoring applications, is driving the need for energy harvesting from natural sources. To fill a gap in the scientific literature, this study quantitatively investigates the advantages of hybrid energy harvesters, which utilize multiple energy sources, compared to single-source harvesters. The analysis leverages a real-world dataset collected from a meteorological station in Cervinara, Southern Italy. The measured data are processed to estimate the energy that can be recovered from solar, wind, and rain sources using energy harvesters designed to supply low-power electronic devices. The available energy serves as the basis for optimizing the sizing of a hybrid energy harvester that effectively integrates the aforementioned energy sources. The system sizing, carried out under the constraint of ensuring a continuous and uninterrupted power supply to the load, quantifies the benefits of using a hybrid harvester over a single-source harvester. The results show that one of the main advantages of the hybrid solution is the reduction in the size of the storage device, enabling the replacement of rechargeable batteries with supercapacitors, which offer both environmental and reliability benefits. Full article

Offshore wind turbines have garnered significant attention recently due to their substantial wind energy harvesting capabilities. Pitch control plays a crucial role in maintaining the rated generator speed, particularly in offshore environments characterized by highly turbulent winds, which pose a huge challenge. Moreover, hydraulic pitch systems are favored in large-scale offshore wind turbines due to their superior power-to-weight ratio compared to electrical systems. In this study, a proportional valve-controlled hydraulic pitch system is developed along with an intelligent pitch control strategy aimed at developing rated power in offshore wind turbines. The proposed strategy utilizes a cascade configuration of an improved recurrent Elman neural network, with its parameters optimized using a customized particle swarm optimization algorithm. To assess its effectiveness, the proposed strategy is compared with two other intelligent pitch control strategies, the cascade improved Elman neural network and cascade Elman neural network, and tested in a benchmark wind turbine simulator. Results demonstrate effective power generation, with the proposed strategy yielding a 78.14% and 87.10% enhancement in the mean standard deviation of generator power error compared to the cascade improved Elman neural network and cascade Elman neural network, respectively. These findings underscore the efficacy of the proposed approach in generating rated power. Full article