Hasil untuk "Production of electric energy or power. Powerplants. Central stations"

Miriana Russo, Corrado Santoro, Federico Fausto Santoro et al.

Viticulture is facing growing economic and environmental pressures that demand a transition toward intelligent and autonomous crop management systems. Phytopathologies remain one of the most critical threats, causing substantial yield losses and reducing grape quality, while regulatory restrictions on agrochemicals and sustainability goals are driving the development of precision agriculture solutions. In this context, early disease detection is crucial; however, current visual inspection methods are hindered by subjectivity, cost, and delayed symptom recognition. This study presents a fully autonomous robotic platform developed within the Agrimet project, enabling continuous, high-frequency monitoring in vineyard environments. The system integrates a tracked mobility base, multimodal sensing using RGB-D and thermal cameras, an AI-based perception framework for leaf localisation, and a compliant six-axis manipulator for biological sampling. A custom control architecture bridges standard autopilot PWM signals with industrial CANopen motor drivers, achieving seamless coordination among all subsystems. Field validation in a Sicilian vineyard demonstrated the platform’s capability to navigate autonomously, acquire multimodal data, and perform precise georeferenced sampling under unstructured conditions. The results confirm the feasibility of holistic robotic systems as a key enabler for sustainable, data-driven viticulture and early disease management. The YOLOv10s detection model achieved good precision and F1-score for leaf detection, while the integrated Kalman filtering visual servoing system demonstrated low spatial tolerance under field conditions despite foliage sway and vibrations.

Yujuan Sun, Shaoyuan You, Fangfang Hu et al.

Accurate estimation of the State of Charge (SOC) for lithium-ion batteries is a core function of the Battery Management System (BMS). However, LiFePO₄ batteries present specific challenges for SOC estimation due to the characteristic plateau in their open-circuit voltage (OCV) versus SOC relationship. Moreover, data-driven estimation approaches often face significant difficulties stemming from measurement noise and interference, the highly nonlinear internal dynamics of the battery, and the time-varying nature of key battery parameters. To address these issues, this paper proposes a Long Short-Term Memory (LSTM) model integrated with feature engineering, physical constraints, and the Extended Kalman Filter (EKF). First, the model’s temporal perception of the historical charge–discharge states of the battery is enhanced through the fusion of temporal voltage information. Second, a post-processing strategy based on physical laws is designed, utilizing the Particle Swarm Optimization (PSO) algorithm to search for optimal correction factors. Finally, the SOC obtained from the previous steps serves as the observation input to EKF filtering, enabling a probabilistically weighted fusion of the data-driven model output and the EKF to improve the model’s dynamic tracking performance. When applied to SOC estimation of LiFePO₄ batteries under various operating conditions and temperatures ranging from 0 °C to 50 °C, the proposed model achieves average Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) as low as 0.46% and 0.56%, respectively. These results demonstrate the model’s excellent robustness, adaptability, and dynamic tracking capability. Additionally, the proposed approach only requires derived features from existing input data without the need for additional sensors, and the model exhibits low memory usage, showing considerable potential for practical BMS implementation. Furthermore, this study offers an effective technical pathway for state estimation under a “physical information–data-driven–filter fusion” framework, enabling accurate SOC estimation of lithium-ion batteries across multiple operating scenarios.

Akeem Babatunde Akinwola, Abdulaziz Alkuhayli

The rapid integration of photovoltaic (PV) generation into modern power networks introduces significant operational challenges, including intermittent power production, uneven charge distribution, and reduced system reliability in multi-battery energy storage systems. Addressing these challenges requires intelligent, adaptive, and physically consistent control strategies capable of operating under uncertain environmental and load conditions. This study proposes a Physics-Informed Neural Network (PINN)-based charge allocation framework that explicitly embeds physical constraints—namely charge conservation and State-of-Charge (SoC) equalization—directly into the learning process, enabling real-time adaptive control under varying irradiance and load conditions. The proposed controller exploits real-time measurements of PV voltage, current, and irradiance to achieve optimal charge distribution while ensuring converter stability and balanced battery operation. The framework is implemented and validated in MATLAB/Simulink under Standard Test Conditions of 1000 W·m⁻² irradiance and 25 °C ambient temperature. Simulation results demonstrate stable PV voltage regulation within the 230–250 V range, an average PV power output of approximately 95 kW, and effective duty-cycle control within the range of 0.35–0.45. The system maintains balanced three-phase grid voltages and currents with stable sinusoidal waveforms, indicating high power quality during steady-state operation. Compared with conventional Proportional–Integral–Derivative (PID) and Model Predictive Control (MPC) methods, the PINN-based approach achieves faster SoC equalization, reduced transient fluctuations, and more than 6% improvement in overall system efficiency. These results confirm the strong potential of physics-informed intelligent control as a scalable and reliable solution for smart PV–battery energy systems, with direct relevance to renewable microgrids and electric vehicle charging infrastructures.

Zening Wang, Yu Tang, Chencheng Shi et al.

Abstract RETRACTION : S. S. M. Javid, G. Derakhshan, S. M. Hakimi: Probability modelling of storage based‐smart energy hub considering electric vehicles charging stations performance and demand side management. IET Renewable Power Generation 17, 3221–3239 (2023).  https://doi.org/10.1049/rpg2.12839 The above article, published online on 29 th August 2023 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal's Editor‐in‐Chief; David Infield; the Institution of Engineering and Technology; John Wiley & Sons Ltd.; and the authors. Since publication, the authors have alerted the journal about substantial text overlap between the article and an unpublished source of a different research group. Following an investigation, the IET and the journal have verified a substantial text overlap. Accordingly, the article is retracted. The authors apologise for the oversight.

Jay Prakash Goit

The current study investigates the effect of terrain features on wind resources in a region with extremely diverse terrain. To that end, a case study of Nepal based on annual wind data collected from 10 different sites is performed. The evaluation of mean wind speeds using Weibull probability density functions (PDFs) shows that complex-terrain sites exhibit greater variability in 10-min average wind speeds relative to the annual average wind speeds. This pattern is also evident in comparisons of short- and long-term average wind speeds. At the complex-terrain sites, the wind speeds exhibited strong short-term variations, suggesting that local terrain effects dominate over seasonal wind variation. Terrain complexity also strongly affected turbulence. The flat-terrain sites showed turbulence intensities below the lowest IEC category turbulence profile, while the complex-terrain sites exceeded the highest IEC profile. This indicates that the IEC standard may require modification based on site complexity parameters, such as the standard deviation of elevation fluctuations. The power law exponent (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>), used to extrapolate wind speeds to higher elevations, deviated notably from the typical 1/7 value, even in flat terrain. Finally, a power potential analysis indicated that three sites with higher mean wind speeds achieved higher capacity factors.

Chuanshuai Dong, Lei Chen, Weiquan Lin et al.

Abstract Interfacial solar evaporation, which captures solar energy and localizes the absorbed heat for water evaporation, is considered a promising technology for seawater desalination and solar energy conversion. However, it is currently limited by its low photothermal conversion efficiency, salt accumulation, and poor reliability. Herein, inspired by human intestinal villi structure, we design and fabricate a novel intestinal villi‐like nitrogen‐doped carbon nanotubes solar steam generator (N‐CNTs SSG) consisting of three‐dimensional (3D) hierarchical carbon nanotube matrices for ultrahigh solar evaporation efficiency. The 3D matrices with radial direction nitrogen‐doped carbon nanotube clusters achieve ultrahigh surface area, photothermal efficiency, and hydrophilicity, which significantly intensifies the whole interfacial solar evaporation process. The new solar evaporation efficiency reaches as high as 96.8%. Furthermore, our ab initio molecular dynamics simulation reveals that N‐doped carbon nanotubes exhibit a greater number of electronic states in close proximity to the Fermi level when compared to pristine carbon nanotubes. The outstanding absorptivity in the full solar spectrum and high solar altitude angles of the 3D hierarchical carbon nanotube matrices offer great potential to enable ultrahigh photothermal conversion under all‐day and all‐season circumstances.

Corentin Piat, Ali Sari, Christophe Viton

New-generation batteries are attracting increasing interest in response to today’s energy storage challenges, as evidenced by the steady rise in scientific publications on the topic. However, their industrial deployment remains limited due to the complexity of aging mechanisms, which are still poorly understood and difficult to control. While several promising developments have emerged in laboratory settings, they remain too immature to be scaled up. These aging processes, which directly affect the performance, safety, and lifespan of battery systems, also determine their technical and economic viability. This review offers a comparative analysis of aging phenomena—both specific to individual technologies and common across systems—drawing on findings from accelerated testing, post-mortem analyses, and modeling. It highlights critical failures such as interface instability, loss of active material, and mechanical stress, while also identifying shared patterns and the unique features of each technology. By combining experimental data with theoretical approaches, the article proposes an integrated framework for understanding and prioritizing aging mechanisms by technology type. It underscores the limitations of current characterization techniques, the urgent need for harmonized testing protocols, and the importance of standardized data sharing. Finally, it outlines possible avenues for improving the understanding and mitigation of aging phenomena.

Rui Xie, Wei Wei

Bensu Prencuva, Alper Cicek, Ozan Erdinc et al.

António J. Arsénio Costa, João F. P. Fernandes, Paulo J. Costa Branco

This paper analyzes the viability of different solutions to passively augment the axial stiffness of a horizontal axis radial levitation passive magnetic bearing (PMB) with a previously studied topology. The zero-field cooling (ZFC) of high-temperature superconductor (HTS) bulks promotes higher magnetic impulsion and levitation forces and lower electromagnetic losses than those with field-cooling (FC) but, on the other hand, the guiding stability is much lower than those with FC. Because of stability reasons, FC was adopted in most superconducting maglev systems. The trend of this research group has been to develop a horizontal axis HTS ZFC radial levitation PMB presenting notable levitation forces with reduced electromagnetic losses, defined by a topology that creates guiding stability. Previous work has shown that optimizing the bearing geometry to maximize magnetic guidance forces might not be enough to guarantee the axial stiffness required for many applications. First, the extent to which guidance forces are augmented by increasing the number of HTS bulks in the stator is evaluated. Then, the axial stiffness augmentation by passively adding two limiting permanent magnet (PM) rings is evaluated. The results show that the axial stiffness is highly augmented by adding limiting PM rings with no significant additional investment. This change enables the use of the studied ZFC superconducting PMB in high-precision axial stability applications, such as precision gyroscopes, horizontal axis propellers, and turbines.

Qing Qin, Zijian Li, Yingzheng Zhang et al.

Abstract Electrocatalytic CO2‐to‐formate conversion is considered an economically viable process. In general, Zn‐based nanomaterials are well‐known to be highly efficient electrocatalysts for the conversion of CO2 to CO, but seldom do they exhibit excellent selectivity toward formate. In this article, we demonstrate that a heterointerface catalyst ZnO/ZnSnO3 with nanosheet morphology shows enhanced selectivity with a maximum Faradaic efficiency (FE) of 86% at −0.9 V versus reversible hydrogen electrode and larger current density for the conversion of CO2 to formate than pristine ZnO and ZnSnO3. In particular, the FEs of the C1 products (CO + HCOO−) exceed 98% over the potential window. The experimental measurements combined with theoretical calculations revealed that the ZnO in ZnO/ZnSnO3 heterojunction delivers the valence electron depletion and accordingly optimizes Zn d‐band center, which results in moderate Zn–O hybridization of HCOO* and weakened Zn–C hybridization of competing COOH*, thus greatly boosting the HCOOH generation. Our study highlights the importance of charge redistribution in catalysts on the selectivity of electrochemical CO2 reduction.

Faiz Ahmad, Peter B Monk, Akhlesh Lakhtakia

The building sector accounts for 36% of energy consumption and 39% of energy-related greenhouse-gas emissions. Integrating bifacial photovoltaic solar cells in buildings could significantly reduce energy consumption and related greenhouse gas emissions. Bifacial solar cells should be flexible, bifacially balanced for electricity production, and perform reasonably well under weak-light conditions. Using rigorous optoelectronic simulation software and the differential evolution algorithm, we optimized symmetric/asymmetric bifacial CIGS solar cells with either (i) homogeneous or (ii) graded-bandgap photon-absorbing layers and a flexible central contact layer of aluminum-doped zinc oxide to harvest light outdoors as well as indoors. Indoor light was modeled as a fraction of the standard sunlight. Also, we computed the weak-light responses of the CIGS solar cells using LED illumination of different light intensities. The optimal bifacial CIGS solar cell with graded-bandgap photon-absorbing layers is predicted to perform with 18%–29% efficiency under 0.01–1.0-Sun illumination; furthermore, efficiencies of 26.08% and 28.30% under weak LED light illumination of 0.0964 mW cm ^−2 and 0.22 mW cm ^−2 intensities, respectively, are predicted.

Qiru Huang, Aixuan Zhao, Wei Wei et al.

Zi-Qi Yang, Mehrdad R. Kermani

The collaborative robot market has experienced rapid growth, leading to advancements in compliant actuation and torque control. Magneto-rheological (MR) clutches offer a hardware-level solution for achieving both compliance and torque control through adjustable coupling between the input and output of the MR clutch. However, the presence of frequency-dependent magnetic hysteresis makes controlling the output torque challenging. In this paper, we present a comparative study of six widely used hysteresis models and propose a computationally efficient algebraic model to address the issue of hysteresis modeling and control of the output torque of rotary MR clutches. We compare the estimated torques with experimental measurements from a prototype MR clutch, to evaluate the computational complexity and accuracy of the model. Our proposed algebraic hysteresis model demonstrates superior accuracy and approximately two times less computational complexity than the Bouc–Wen model, and approximately twenty times less memory requirement than neural network-based models. We show that our proposed model has excellent potential for embedded indirect torque control schemes in systems with hysteresis, such as MR clutches and isolators.

Mirella Lima Saraiva Araujo, Yasmin Kaore Lago Kitagawa, Arthur Lúcide Cotta Weyll et al.

Wind power forecasting is pivotal in promoting a stable and sustainable grid operation by estimating future power outputs from past meteorological and turbine data. The inherent unpredictability in wind patterns poses substantial challenges in synchronizing supply with demand, with inaccuracies potentially destabilizing the grid and potentially causing energy shortages or excesses. This study develops a data-driven approach to forecast wind power from 30 min to 12 h ahead using historical wind power data collected by the Supervisory Control and Data Acquisition (SCADA) system from one wind turbine, the Enercon/E92 2350 kW model, installed at Casa Nova, Bahia, Brazil. Those data were measured from January 2020 to April 2021. Time orientation was embedded using sine/cosine or cyclic encoding, deriving 16 normalized features that encapsulate crucial daily and seasonal trends. The research explores two distinct strategies: error prediction and error correction, both employing a sequential model where initial forecasts via k-Nearest Neighbors (KNN) are rectified by the Extra Trees Regressor. Their primary divergence is the second model’s target variable. Evaluations revealed both strategies outperforming the standalone KNN, with error correction excelling in short-term predictions and error prediction showing potential for extended forecasts. This exploration underscores the imperative importance of methodology selection in wind power forecasting.

Ziwei Wang, Wenliang Yin, Lin Liu et al.

A hybrid drive wind turbine equipped with a speed regulating differential mechanism can generate electricity at the grid frequency by an electrically excited synchronous generator without requiring fully or partially rated converters. This mechanism has extensively been studied in recent years. To enhance the transient operation performance and low-voltage ride-through capacity of the proposed hybrid drive wind turbine, we aim to synthesize an advanced control scheme for the flexible regulation of synchronous generator excitation based on fractional-order sliding mode theory. Moreover, an extended state observer is constructed to cooperate with the designed controller and jointly compensate for parametric uncertainties and external disturbances. A dedicated simulation model of a 1.5 MW hybrid drive wind turbine is established and verified through an experimental platform. The results show satisfactory model performance with the maximum and average speed errors of 1.67&#x0025; and 1.05&#x0025;, respectively. Moreover, comparative case studies are carried out considering parametric uncertainties and different wind conditions and grid faults, by which the superiority of the proposed controller for improving system on-grid operation performance is verified.

Yanchun XU, Zhongyao FAN, Sihan SUN et al.

Since the fault current output from the doubly-fed wind farm has frequency deviation characteristics and contains large harmonic components, when an internal fault occurs in the transmission transformer for the doubly-fed wind farm, the ratio of the second harmonic to the fundamental wave in the differential current of the transformer increases, which makes the differential protection of the transformer face the risk of delay action. Moreover, when the system fails, a large number of non-periodic components in the fault current output from the doubly-fed wind farm will make the current transformer in the transmission transformer more prone to saturation, resulting in reduced reliability of the differential protection of traditional transformers. This paper proposes a differential protection scheme for the transmission transformer for large-scale wind farms based on detrended analysis. Firstly, the sampling current is processed by detrended analysis through the sliding data window to obtain the detrended residual function, and then the slope characteristics of the current waveform are utilized to complete the effective distinction between the magnetizing inrush current and the fault differential current (including the current transformer saturation state) of the transformer. The proposed protection scheme is validated to be applicable under different operating conditions by building a transmission system for the doubly-fed wind farm in PSCAD.

Junhong Guo, Fan Feng, Shiqiang Zhao et al.

Abstract Sodium iron hexacyanoferrate (FeHCF) is one of the most promising cathode materials for sodium‐ion batteries (SIBs) due to its low cost theoretical capacity. However, the low electrochemical activity of FeLS(C) in FeHCF drags down its practical capacity and potential plateau. Herein, FeHCF with high FeLS(C) electrochemical activity (C‐FeHCF) is synthesized via a facile citric acid‐assisted solvothermal method. As the cathode of SIBs, C‐FeHCF shows superior cycling stability (ca. 87.3% capacity retention for 1000 cycles at 10 C) and outstanding rate performance (ca. 68.5% capacity retention at 50 C). Importantly, the contribution of FeLS(C) to the whole capacity was quantitatively analyzed via combining dQ/dV and discharge curve for the first time, and the index reaches 44.53% for C‐FeHCF, close to the theoretical value. In‐situ X‐ray diffraction proves the structure stability of C‐FeHCF during charge–discharge process, ensuring its superior cycling performance. Furthermore, the application feasibility of the C‐FeHCF cathode in quasi‐solid SIBs is also evaluated. The quasi‐solid SIBs with the C‐FeHCF cathode exhibit excellent electrochemical performance, delivering an initial discharge capacity of 106.5 mAh g−1 at 5 C and high capacity retention of 89.8% over 1200 cycles. This work opens new insights into the design and development of advanced cathode materials for SIBs and the beyond.

Xuan Deng, Yufan Hu, Yiyang Jia et al.

Abstract The social economy is growing rapidly, and the power grid load demand is increasing. To maintain the stability of the power grid, it is crucial to achieve accurate and rapid power system stability assessment. In the actual operation of the power network, data loss is an unavoidable situation. However, most of the data‐driven models currently used assume that the input data is complete, which has obvious limitations in real‐world applications. This paper suggests an IVS‐GAN model to assess power system stability using incomplete phasor measurement unit measurement data with random loss. The proposed method combines the super‐resolution perception technology based on generative adversarial network (GAN) with a time‐series signal classification model. The generator adopts a 1D U‐Net network and uses convolutional layers to complete and recover missing data. The discriminator adopts a new gated recurrent unit–attention architecture proposed here to better extract voltage temporal variation features on key buses. The result of this paper is that the stability evaluation method outperforms other algorithms in high voltage data loss rates on the New England 10‐machine 39‐bus system.