Output Power Research Articles

Predicting power and solar energy using neural networks and PCA with meteorological parameters from Diass and Taïba Ndiaye

The excessive reliance on conventional fossil fuel-based resources poses a significant threat to our environment. To mitigate this impact, it has become increasingly crucial to increase the integration of intermittent and non-polluting energy sources into our electrical grids. However, while this higher penetration rate brings benefits such as improved producer satisfaction and reduced fossil fuel consumption, it also presents challenges for traditional non-smart electrical networks. To promote intermittent energy sources effectively and maintain a balance between consumption and production, accurate forecasting of these energy outputs plays a vital role. This research paper focuses on studying the application of artificial neural networks for predicting the power and energy output of the Diass solar power plant in the short and medium term. The proposed approach utilizes not only the meteorological data from the city where the power plant is located but also data from a nearby city with a data acquisition station. Principal component analysis (PCA) is employed to select the relevant variables for the prediction model. Furthermore, the results obtained from our approach are compared to existing literature that solely uses meteorological data from the power plant's location. The comparison shows that our method achieves more satisfactory results, with mean absolute errors and root mean square errors of 0.0223 KWh and 0.003 KWh, respectively, and a prediction accuracy of 94.57% in terms of energy and power. It is worth noting that the computational resource requirements for our approach are higher, with simulation times ranging between 1788 seconds and 2201 seconds. By utilizing a broader range of data sources and employing advanced techniques like artificial neural networks, this research contributes to improving the accuracy of solar power generation forecasts. The findings highlight the potential of incorporating additional data inputs and advanced modeling techniques to enhance the performance of renewable energy systems, paving the way for a more sustainable and efficient energy future.

Comprehensive study on the efficiency of vertical bifacial photovoltaic systems: a UK case study

This paper presents the first comprehensive study of a groundbreaking Vertically Mounted Bifacial Photovoltaic (VBPV) system, marking a significant innovation in solar energy technology. The VBPV system, characterized by its vertical orientation and the use of high-efficiency Heterojunction cells, introduces a novel concept diverging from traditional solar panel installations. Our empirical research, conducted over a full year at the University of York, UK, offers an inaugural assessment of this pioneering technology. The study reveals that the VBPV system significantly outperforms both a vertically mounted monofacial PV (VMPV) system and a conventional tilted monofacial PV (TMPV) system in energy output. Key findings include a daily power output increase of 7.12% and 10.12% over the VMPV system and an impressive 26.91% and 22.88% enhancement over the TMPV system during early morning and late afternoon hours, respectively. Seasonal analysis shows average power gains of 11.42% in spring, 8.13% in summer, 10.94% in autumn, and 12.45% in winter compared to the VMPV system. Against the TMPV system, these gains are even more substantial, peaking at 24.52% in winter. These results underscore the VBPV system's exceptional efficiency in harnessing solar energy across varied environmental conditions, establishing it as a promising and sustainable solution in solar energy technology.

All-back-contact neutral-colored transparent crystalline silicon solar cells enabling seamless modularization

Transparent solar cells (TSCs) hold substantial potential as continuous energy generators, enabling their use in situations where conventional devices may not be feasible. However, research aimed at modularizing TSCs for the purpose of regulating the overall voltage and current they produce, a critical step toward practical application, is still in its nascent stages. In this study, we explored a custom-designed, all-back-contact (ABC) configuration, which situates all electrical contacts on the rear side, to create glass-like transparent crystalline silicon (c-Si) solar cells and seamless modules. The ABC design not only demonstrates high power conversion efficiency (PCE) in solar cells but also ensures unobstructed visibility through transparent solar modules. Notably, ABC-transparent c-Si solar cells achieved a peak PCE of 15.8% while maintaining an average visible transmittance of 20%. Through seamlessly interconnecting the unit cells, the output voltage and power were systematically tuned from 0.64 V and 15.8 mW (for a 1 cm2-sized unit cell) to 10.0 V and 235 mW (for a 16 cm2-sized module). Furthermore, we successfully demonstrated the photocharging of a smartphone using a transparent ABC solar module.

The Impact of Low-Power Therapeutic Lasers at 904 nm on the Healing Process of Wounds and the Relationships Between Extracellular Matrix Components and Myofibroblasts.

The goal of the study was to further our understanding of the functions that myofibroblasts do, how they interact with the matrix's extracellular elements, and how laser therapy at 904 nm works. Thirty Wistar rats with superficial wounds on the backs were used in this study. The laser therapy device used two energy levels of a 904 nm laser with an output power of 60 mW. The outcomes had been evaluated for one, three, five, seven, and fourteen days following laser therapy. Numerous methods, including as histology, immunohistochemistry, and scanning electron microscopy, have been used to evaluate the tissues.There was a statistically important (P ≤ .05) decrease in the number of inflammatory cells and the degree of edema after laser therapy. On the other hand, the amounts of elastic fiber as well as collagen slightly increased. On the third post-laser treatment day, there was a statistically significant distinction (P < .05) in the number of myofibroblasts with the desmin; smooth muscle alpha-actin phenotype between the laser-treated and control groups.These results suggest that, compared to a higher energy of 5.6 J, laser therapy at a lower energy of 3.6 J may be more effective in stimulating myofibroblast differentiation during the initial phases of wound healing. These findings show how low-power laser therapy promotes wound healing.

Experimental validation of a low-cost maximum power point tracking technique based on artificial neural network for photovoltaic systems

Maximum power point tracking (MPPT) is a technique involved in photovoltaic (PV) systems for optimizing the output power of solar panels. Traditional solutions like perturb and observe (P&O) and Incremental Conductance (IC) are commonly utilized to follow the MPP under various environmental circumstances. However, these algorithms suffer from slow tracking speed and low dynamics under fast-changing environment conditions. To cope with these demerits, a data-driven artificial neural network (ANN) algorithm for MPPT is proposed in this paper. By leveraging the learning capabilities of the ANN, the PV operating point can be adapted to dynamic changes in solar irradiation and temperature. Consequently, it offers promising solutions for MPPT in fast-changing environments as well as overcoming the limitations of traditional MPPT techniques. In this paper, simulations verification and experimental validation of a proposed data-driven ANN-MPPT technique are presented. Additionally, the proposed technique is analyzed and compared to traditional MPPT methods. The numerical and experimental findings indicate that, of the examined MPPT methods, the proposed ANN-MPPT approach achieves the highest MPPT efficiency at 98.16% and the shortest tracking time of 1.3 s.

Impact of shading heaviness on voltage, current and power of the solar photovoltaic string

The short-term power output variability of solar photovoltaic (PV) systems caused by passing clouds is becoming a major concern for grid operators. As the penetration of utility-scale PV systems boosts, the rapid power fluctuations greatly challenge the grid's transient stability. A photo voltaic system is greatly vulnerable to Partial Shading. The performance analysis of PV systems clearly suggests that the maximum power of partially shaded PV systems is inversely proportional to the heaviness of shading. However, some literatures are not of the same opinion; for them, the maximum power of a partially shaded system is invulnerable to shading heaviness, often at certain critical points. According to research on the P-V characteristic curve under various numbers of shaded modules and shading levels, the irradiance of the shaded modules reaches a certain turning point or critical point. The PV array becomes insensitive to high-level shading, categorically for the series topology. This paper presents an experimental results of performance parameters with varying shading heaviness. The experiment is performed on a Photovoltaics string of series connected modules, a 119KW grid-connected rooftop solar power plant using the Power Quality analyser for continuous recording of various parameters under changing shading heaviness. The degree or heaviness of shading decides the critical point. The critical point can vary based on the number of the shaded modules.

Power Forecasting for Photovoltaic Microgrid Based on MultiScale CNN-LSTM Network Models

Photovoltaic (PV) microgrids comprise a multitude of small PV power stations distributed across a specific geographical area in a decentralized manner. Computational services for forecasting the output power of power stations are crucial for optimizing resource deployment. This paper proposes a deep-learning-based architecture for short-term prediction of PV power. Firstly, in order to make full use of the spatial information between different power stations, a spatio–temporal feature fusion method is proposed. This method is capable of exploiting both the power information of neighboring power stations with strong correlations and meteorological information with the PV feature data of the target power station. By using a multiscale convolutional neural network–long short-term memory (CNN-LSTM) network model, it is capable of generating a PV feature dataset containing spatio–temporal attributes that expand the data source and enhance the feature constraints. It is capable of predicting the output power sequences of power stations in PV microgrids with high model generalization and responsiveness. To validate the effectiveness of the proposed framework, an extensive numerical analysis is also conducted based on a real-world PV dataset.

A piezoelectric-electromagnetic hybrid energy harvester for rotational motion driven by magnetic repulsion

Harvesting energy from rotational motion is an efficient and widely used technology that provides power support for various electronic devices and systems by converting mechanical kinetic energy into electrical energy. This energy harvesting method shows great potential and advantages in industrial automation, transportation, wind power generation, and smart homes. This study proposes a piezoelectric-electromagnetic hybrid energy harvester (HEH) for rotational motion driven by magnetic repulsion. HEH comprises of two parts: a piezoelectric energy harvester (PEH) and an electromagnetic energy harvester (EMH). HEH utilizes the magnetic drive reciprocating motion to enhance the energy collection efficiency of PEH and EMH. Additionally, by adding the magnetic effect to the ends of bimorph piezoelectric sheets, the spectrum of energy harvesting is expanded. Its parameters are analyzed using theoretical analysis and simulation, and an experimental testbed is established to explore the influence of HEH output performance. The results indicate that the output power reaches its maximum when there are 2 circular magnets on the rotor, the gap distance of magnets is 15 mm, and 2 mass blocks at the end of the bimorph piezoelectric sheet. The PEH and EMH outputs are 173.36 V and 4.81 V, respectively. The maximum output power of HEH is 53.45 mW. The power density can reach 6.818 mW cm−3. Compared with PEH and EMH, the output performance is improved by 46.94% and 174.95%, respectively. When the rotation speed is 500 r min−1, HEH can effortlessly light up 80 LEDs. The experimental results all demonstrate the potential of HEH to power low-power sensors.

Enhanced In‐Plane Omnidirectional Energy Harvesting from Extremely Weak Magnetic Fields via Fourfold Symmetric Magneto‐Mechano‐Electric Coupling

AbstractMagneto‐mechano‐electric (MME) energy harvesters (EHs) have emerged as a promising solution for powering Internet of Things (IoT) sensor networks by capturing ambient stray magnetic fields in urban circumstance. Despite significant advancements, achieving milliwatt‐level power generation from extremely weak (&lt;1 Oe) and randomly oriented magnetic fields remains challenging. Drawing inspiration from the configuration of cross‐shaped spider legs, this study introduces an innovative X‐shaped MME‐EH featuring fourfold symmetric architecture with symmetrically distributed magnets at each end, enabling near‐isotropic in‐plane omnidirectional energy harvesting. Under extremely weak magnetic fields 0.75 and 0.3 Oe at 60 Hz, the device achieves a record‐high output power of 7.31 and 1.67 mWRMS, respectively, representing a 362% improvement in normalized power over the state‐of‐the‐art results. The theoretical model attributes these gains to the synergistic enhancement effect of the second bending mode and the dual‐mode structure, collectively improving both magnetomechanical and electromechanical coupling by augmenting the magnetic moments and suppressing the clamp loss. The device further demonstrates robust real‐world applications, efficiently powering a wireless IoT sensor system by harvesting multidirectional magnetic field energy from household appliances. This work opens new avenues for developing efficient multimodal MME‐EHs capable of harnessing omnidirectional, weak magnetic fields for widespread IoT applications.

A Resonance‐Based Study of the Output Characteristics of Spring‐Assisted Triboelectric Nanogenerator

The triboelectric nanogenerator (TENG) is a new type of energy conversion technology capable of transforming various forms of environmental energy into electricity. However, most of the existing spring‐assisted TENGs (S‐TENGs) are based on the vertical contact‐separation mode, which has low energy‐harvesting efficiency and insufficient research on the performance output of TENG under near‐resonant frequency conditions. In this article, a low‐cost S‐TENG with independent layer mode is designed for vibration energy harvesting. The effects of different vibration parameters and structural parameters on the output performance are comprehensively investigated. In the experimental results, it is shown that the output voltage of the S‐TENG reaches its peak at a frequency of 50 Hz, achieving ≈40 V. To validate the capability of S‐TENG in powering low‐power devices, 20 LED lights are successfully lit. It is found that the maximum output power across the external resistor of 8 MΩ is 0.4 mw. It is also investigated that the output characteristics of S‐TENG under resonance and the results showed that higher output electric power can be achieved when the vibration frequency is close to the intrinsic frequency of the S‐TENG. In this finding, the potential of S‐TENG in optimized energy‐harvesting applications, particularly in resonance‐enhanced scenarios.

Nonlinear optical response and application of indirect narrow-bandgap SbTe nanosheets

The nonlinear optical properties of antimony telluride (SbTe) crystal with indirect narrow bandgap are investigated, which exhibits superior performance for nonlinear laser modulation in the infrared band. The nonlinear optical coefficients of SbTe nanosheets are determined to be −3.3 cm/GW, −2.3 cm/GW, −1.9 cm/GW and the modulation depths are 5.5 %, 2.0 %, 1.5 % at 400 nm, 800 nm, 1064 nm, respectively. Based on the SbTe-SA, a Yb:GdScO3 laser centered at 1064 nm is achieved with 633 mW maximum output power, 56 fs pulse width and 104 MHz repetition rate. The damage threshold of the SbTe nanosheets is calculated to be exceed 338 GW/cm2, revealing the great potential in high-power laser modulation. This investigation marks the inaugural theoretical and experimental analysis of SbTe’s optical properties and its application in laser modulation, laying the groundwork for future research into indirect narrow-bandgap materials in laser technology.

Front Deflector Effects on the Aerodynamic Characteristics of Horizontal Axis Wind Turbines: A Reynolds‐Averaged Navier–Stokes Simulation Study

Flow control devices have garnered significant attention from domestic and international wind energy scholars. In an effort to capture more wind energy and boost the output power of wind turbines, this study incorporates flow control devices into the upstream area of the wind turbine. The study then assesses the impact of the spacing, tilt angle, and height of the flow control devices on the turbine's performance. The study demonstrates that the addition of flow control devices does lead to a notable enhancement in wind turbine power, with a maximum power output growth rate of 9.74%. The maximum growth rates for the average output power of the wind turbine due to the three factors (α, H, and Lw) are 4.20%, 3.76%, and 3.54%, respectively. The degree of influence of the three factors is as follows: α &gt; H &gt; Lw.

Forecasting intraday power output by a set of PV systems using recurrent neural networks and physical covariates

Forecasting intraday power output by a set of PV systems using recurrent neural networks and physical covariates

The Ex-Timing trial: evaluating morning, afternoon, and evening exercise on the circadian clock in individuals with type 2 diabetes and overweight/obesity—a randomized crossover study protocol

BackgroundExercise is known to provide multiple metabolic benefits such as improved insulin sensitivity and glucose control in individuals with type 2 diabetes mellitus (T2DM) and those at risk. Beyond the traditional exercise dose, exercise timing is perceived as a contemporary hot topic, especially in the field of T2DM; however, the number of intervention studies assessing exercise timing and glucose metabolism is scarce. Our aim is to test the effect of exercise timing (i.e., morning, afternoon, or evening) on the inter-individual response variability in glycemic control and related metabolic health parameters in individuals with T2DM and those at risk during a 12-week intervention.MethodsA randomized crossover exercise intervention will be conducted involving two groups: group 1, individuals with T2DM; group 2, age-matched older adults with overweight/obesity. The intervention will consist of three 2-week blocks of supervised post-prandial exercise using high-intensity interval training (HIIT). Between each training block, a 2-week washout period, where participants avoid structured exercise, will take place. Assessments will be conducted in both groups before and after each exercise block. The primary outcomes include the 24-h area under the curve continuous glucose monitoring-based glucose. The secondary outcomes include body composition, resting energy expenditure, insulin response to a meal tolerance test, maximal aerobic capacity, peak power output, physical activity, sleep quality, and insulin and glucose levels. All primary and secondary outcomes will be measured at each assessment point.DiscussionOutcomes from this trial will provide us additional insight into the role of exercise timing on the inter-individual response variability in glycemic control and other related metabolic parameters in two distinct populations, thus contributing to the development of more effective exercise prescription guidelines for individuals with T2DM and those at risk.Trial registrationClinicalTrials.gov NCT06136013. Registered on November 18, 2023.

A Review of Thermal Management and Heat Transfer of Lithium-Ion Batteries

With the increasing demand for renewable energy worldwide, lithium-ion batteries are a major candidate for the energy shift due to their superior capabilities. However, the heat generated by these batteries during their operation can lead to serious safety issues and even fires and explosions if not managed effectively. Lithium-ion batteries also suffer from significant performance degradation at low temperatures, including reduced power output, a shorter cycle life, and reduced usable capacity. Deploying an effective battery thermal management system (BTMS) is crucial to address these obstacles and maintain stable battery operation within a safe temperature range. In this study, we review recent developments in the thermal management and heat transfer of Li-ion batteries to offer more effective, secure, and cost-effective solutions. We evaluate different technologies in BTMSs, such as air cooling, liquid cooling, phase change materials, heat pipes, external preheating, and internal preheating, discussing their advantages and disadvantages. Through comparative analyses of high-temperature cooling and low-temperature preheating, we highlight the research trends to inspire future researchers. According to the review of the literature, submerged liquid BTMS configurations show the greatest potential as a research focus to enhance thermal regulation in Li-ion batteries. In addition, there is considerable research potential in the innovation of air-based BTMSs, the optimization of liquid-based BTMSs, the coupling of heat pipes with PCMs, the integration of PCMs and liquid-cooled hybrid BTMSs, and the application of machine learning and topology optimization in BTMS design. The application of 3D printing in lithium-ion battery thermal management promises to enhance heat transfer efficiency and system adaptability through the design of innovative materials and structures, thereby improving the battery’s performance and safety.

Study of a Novel Updraft Tower Power Plant Combined with Wind and Solar Energy

This study presents a novel solar updraft tower power plant (SUTPP) system, which has been designed to achieve the simultaneous utilization of solar and wind energy resources in desert regions, in response to the pressing demand for sustainable and efficient renewable energy solutions. The aim of this research was to develop an integrated system that is capable of harnessing and converting these abundant energy sources into electrical power, thereby enhancing the renewable energy portfolio in arid environments. The methodology of this study involved the design and construction of a prototype SUTPP, comprising a 53 m high tower, a 6170 m2 collector, five horizontal-axis wind turbines, and a thermal energy storage layer made up of pebbles and sand. The experimental setup was meticulously detailed, and experiments were conducted to collect data on the system’s performance under various environmental conditions. Subsequently, three-dimensional numerical simulations were performed to explore the effects of ambient wind speed and solar radiation on the output power of the SUTPP. The results indicate that the output power of the system increases with the increase in ambient wind speed and solar radiation. The impact of solar irradiation on output power was observed to diminish as ambient wind speeds increased. Notably, as the inlet wind speed rose from 4 m/s to 12 m/s, the output power showed a substantial increase of 727%. The numerical simulations revealed that ambient wind speed has a more pronounced effect on power output compared to solar radiation. Furthermore, it was found that the influence of solar radiation is significant at low wind speeds, with its impact decreasing as wind speed increases. This research provides essential guidance for the design and engineering of highly efficient solar thermal energy utilization projects, representing a significant advancement in the field of renewable energy technology deployment in desert environments.

Applying a Current Sharing Method Based on Partial Energy Processing to Multiphase LLC Resonant Converters

In this paper, partial energy processing is applied to the current sharing technique for multiphase LLC resonant converters. The proposed circuit consists of an LLC resonant converter and a flyback converter, where the flyback converter is only used for partial energy processing. The input voltage of the LLC resonant converter is fine-tuned by the flyback converter to solve the problem of a voltage gain difference between the two phases of the LLC resonant converter caused by the error of the resonant tank components, which prevents the output current from being nonequalized. Since the compensation power is much smaller than the output power, and only one phase will be during circuit operation, the impact on the overall efficiency is minimal. Due to the low dependence between the LLC resonant converter and the flyback converter, they are operated at different switching frequencies. In addition, due to the low dependence between each phase, the circuit can be expanded using odd and even phases.

An Improved Cascaded Boost Converter with an Ultra-High Voltage Gain Suitable for Dielectric Quality Tests

Dielectric quality tests require a high AC voltage with a frequency range of 0.0001 Hz to 1000 Hz. However, providing a high AC voltage with such a frequency variety is challenging. Providing a high DC voltage and then applying such a voltage to an inverter to adjust the frequency can be an acceptable solution for such a challenge. Notably, a high DC voltage is required for DC tests. This study proposes an improved form of the cascaded boost converter, whose merits are as follows: (i) the high voltage gain providing low duty cycles is possible; (ii) the input current is continuous, which decreases the current ripple of the input filter capacitor; (iii) the current stress of the semiconductors is less than the input current, and most of them have a large difference with it; (iv) the voltage stress of the semiconductors is less than the output voltage with a large difference; (v) only one switch with a simple drive circuit is used; (vi) the common ground of the load and input source decreases the EMI noise; (vii) besides the high voltage gain, the voltage density of the converter based on the number of inductors, capacitors, switches, diodes, and whole components is greater than that of the recently proposed converters; (viii) only two stacked connections of the proposed topology can provide a 2.6 kV voltage for a higher DC voltage test of dielectrics. The functional details of the converter are extracted in ideal and continuous conduction (CCM) modes. Moreover, the converter’s voltage gain and density are compared with the recently proposed converters to show the superiority of the proposed converter. Finally, the experimental results are presented to validate the theoretical relations in a 140 W output power.

Poly(benzodifurandione) Coated Silk Yarn for Thermoelectric Textiles.

Thermoelectric textile devices represent an intriguing avenue for powering wearable electronics. The lack of air-stable n-type polymers has, until now, prevented the development of n-type multifilament yarns, which are needed for textile manufacturing. Here, the thermomechanical properties of the recently reported n-type polymer poly(benzodifurandione) (PBFDO) are explored and its suitability as a yarn coating material is assessed. The outstanding robustness of the polymer facilitates the coating of silk yarn that, as a result, displays an effective bulk conductivity of 13 S cm-1, with a projected half-life of 3.2 ± 0.7 years at ambient conditions. Moreover, the n-type PBFDO coated silk yarn with a Young's modulus of E = 0.6GPa and a strain at break of εbreak = 14% can be machine washed, with only a threefold decrease in conductivity after seven washing cycles. PBFDO and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) coated silk yarns are used to fabricate two out-of-plane thermoelectric textile devices: a thermoelectric button and a larger thermopile with 16 legs. Excellent air stability is paired with an open-circuit voltage of 17mV and a maximum output power of 0.67 µW for a temperature difference of 70 K. Evidently, PBFDO coated multifilament silk yarn is a promising component for the realization of air stable thermoelectric textile devices.

Theoretical Models for Performance Analysis of Spintronic THz Emitters

The terahertz (THz) region of the electromagnetic spectrum, spanning from 0.1 to 10 THz, offers unique opportunities for imaging, spectroscopy, and communication applications. However, the potential of THz technologies has been limited by the availability of efficient and versatile THz emitters. Spintronic THz emitters (STEs), leveraging the ultrafast dynamics of electron spins in magnetic materials, have emerged as a promising solution to this challenge. STEs offer significant advantages, including broad bandwidth, high power output, and room-temperature operation, positioning them at the forefront of THz technology development. Despite these advances, understanding the operational principles and improving the performance of STEs remain areas of active research. This review focuses on the theoretical models that describe the behavior of STEs, aiming to provide a comprehensive overview of the underlying physics and suggest directions for future enhancements. Through a detailed examination of these models, the review seeks to clarify the basics of the physics driving STE performance and highlight innovative strategies for their optimization and application expansion.