Search Results (542)

Energy management of multi-carrier energy hubs (MCEHs) is a challenging task, particularly when fuel cell electric vehicle (FCEV) stations are included, due to the stochastic nature of FCEV demand, system loads, and integrated renewable energy resources (RERs) such as wind turbines (WTs) and photovoltaic (PV) systems. This paper aims to optimize the energy management of an MCEH-based microgrid to simultaneously minimize total operating costs and emissions. To this end, a novel enhanced quadratic interpolation optimization (EQIO) algorithm is proposed. The proposed EQIO algorithm incorporates two key improvements: a best-to-mean quasi-oppositional-based learning (BMQOBL) strategy and an evaluation mutation (EM) strategy. The performance of EQIO is evaluated using the CEC 2022 benchmark functions, and the obtained results are compared with those of other optimization techniques. Three case studies are investigated: (i) energy management of the MCEH microgrid without RERs, (ii) sustainable operation (with RERs), and (iii) sustainable operation with RERs combined with the application of demand-side response (DSR). Moreover, the proposed framework explicitly supports long-term sustainability goals by enhancing renewable energy utilization, reducing the carbon footprint, and promoting cleaner transportation through efficient integration of FCEV infrastructure. The results demonstrate that integrating RERs reduces operating costs and emissions by 51.47% and 59.69%, respectively, compared to the case without RERs. Furthermore, the combined application of RERs and DSR achieves cost and emission reductions of 55.26% and 53.93%, respectively, compared to the case without RERs. Full article

This study investigates hot-pressed composite plates manufactured from pellets obtained by mechanical recycling of post-consumer textile waste and reinforced with ground glass-fiber-reinforced polymer (GFRP) originating from wind turbine blades. Composite plates with dimensions of 200 × 330 × 8 mm were produced by hot pressing at 240 °C under 2 MPa with a heating and pressing time of 40 min. The recycled textile-derived polymer blend served as the matrix, while ground GFRP was introduced at 0, 10, 20, and 30 wt.%. Mechanical performance was evaluated using flexural and Charpy impact tests. The composites exhibited flexural strengths in the range of 9–13 MPa and impact strengths of 7.3–8.9 kJ m⁻². The results did not reveal a monotonic increase in flexural strength with increasing reinforcement content. The highest average flexural strength was observed for the unreinforced matrix, while the addition of ground GFRP resulted in comparable or slightly lower strength values accompanied by increased scatter at higher reinforcement levels. The observed behaviour may be associated with heterogeneous dispersion of ground GFRP fragments, reduced effective reinforcement length due to mechanical grinding, interfacial constraints, and defect formation within the press-consolidated structure. The findings provide insight into the structure–property relationships of recycled composite systems based on heterogeneous textile-derived polymer blends. Full article

In traditional wind turbine (WT) operation and maintenance, fault diagnosis and repair have long been relied on, yet the demand for continuous operation under faults persists. To address this, this study proposes a triggered fault-tolerant control framework for wind turbines with zonotope-based interval estimation. The method enhances safety from point to range estimation of FDI, reduces network traffic load via a WT load region-based adaptive event-triggered mechanism, and enables fast, robust fault diagnosis/isolation using interval residuals. A damage equivalent load (DEL)-sensitive cost term balances structural fatigue suppression while ensuring power tracking and safety constraints. Theoretically, Linear Matrix Inequality (LMI) conditions based on common quadratic Lyapunov ensure closed-loop stability and bounded observation errors, with proven interval residual fault sensitivity and triggering reliability. Numerically, on the standard NREL 5-MW WT model under multi-conditions (turbulence, faulty communication), it achieves an average power tracking accuracy of 95.56%, 28.68% fatigue suppression, and 67.40% bandwidth saving. Overall, it synergistically optimizes robust estimation, economical communication, and fatigue-aware control, providing a theoretically rigorous and experimentally validated technical framework for engineering-scale WT reliability improvement and lifespan extension. Full article

Approximately one quarter of the European Union’s (EU’s) CO₂ emissions originate from the transport sector, of which road transport, such as cars and heavy-duty vehicles, contributes roughly 72%. Moreover, according to the European Automobile Manufacturers’ Association, 92% of cars in the EU are internal combustion engine vehicles powered by fossil fuels. Therefore, boosting the adoption of Electric Vehicles (EVs) is considered one of the most prominent solutions for reducing GHG emissions and achieving the EU’s climate targets. To increase EV adoption and fulfil the demand of EV users, adequate EV Charging Stations (EVCSs) are required. Nevertheless, since most EVCSs are supplied by electricity grids that remain predominantly fossil fuel-based, their operation entails substantial indirect GHG emissions. A prominent approach to reducing grid-related emissions is integrating renewable energy sources (RESs) with EVCSs, thereby lowering emissions and alleviating grid stress. Although promising, the energy, economic, and environmental (3E) benefits of this integration remain insufficiently explored. Therefore, this study develops and applies a 3E optimisation framework to assess the feasibility and performance of RES-powered EVCS at NOVA University Lisbon (UNL). Data was collected from the UNL parking area, such as time of arrival, and time of departure. Also, a rule-based algorithm was developed to curate data and estimate the EVCS load profile. Furthermore, HOMER optimisation software was employed to evaluate four scenarios, including (i) an EVCS based on PV, Wind Turbine (WT), and the grid, (ii) an EVCS based on PV and the grid, (iii) an EVCS based on WT and the grid, and (iv) an EVCS based only on energy withdrawal from the grid (base scenario). Under the adopted techno-economic assumptions, in the most optimised scenario, economic and environmental analyses illustrate significant improvements over the base scenario: CO₂ emissions are five times lower, and cost of energy is significantly lower, resulting in significantly lower EV charging costs for users. The results demonstrate that, through developed feasibility studies, researchers, decision-makers, and stakeholders can reach better conclusions about EVCS planning and management. Full article

The quality of wind power output data directly impacts the assessment of wind farm operational status and the accuracy of power forecasting models. However, due to factors such as sensor precision, communication interference, and the complex harbor environment, raw data collected from port-area wind turbines often contain noise, outliers, and missing values. Without effective cleaning, the resulting power curves can be distorted, reducing the generalization capability of predictive models. To overcome the limitations of traditional outlier detection methods in terms of adaptability and robustness, this study proposes a two-stage port-area wind power data cleaning approach based on dynamic interquartile range and an improved Sigmoid function fitting. In the first stage, an adaptive binning and density-weighting mechanism dynamically expands the interquartile range to identify and remove local outliers across different wind speed intervals. In the second stage, the cleaned wind speed–power data are subjected to secondary fitting and residual analysis using an improved Sigmoid model to detect hidden anomalies and boundary-type outliers. Using measured data from the #1 WT in the Chuanshan Port area as a case study, the experimental results demonstrate that the proposed method achieves high data retention while outperforming the conventional interquartile range, density-based spatial clustering of applications with noise and isolation forest algorithms in terms of the Pearson correlation coefficient (r = 0.93) and the coefficient of determination (R² = 0.89), with mean squared error and root mean squared error reduced to 446.39 kW and 545.58 kW, respectively. The findings verify the efficiency, stability, and practical feasibility of the method for port-area wind power data cleaning, providing a reliable data foundation for wind power forecasting and operational optimization in port environments. Full article

This study investigates the material recycling potential of discarded wind instrument reeds (Arundo donax), which are conventionally incinerated, by compounding them with thermoplastics (thermoplastic polyolefin, TPO; polybutylene succinate, PBS). After recovered reeds were pulverized and injection-molded at 10 and 30 wt% concentrations, their mechanical and interfacial properties were evaluated. Experimentally obtained results indicate that waste reeds function as effective reinforcing agents, particularly when combined with biodegradable PBS. Incorporating 30 wt% reed flour into PBS enhanced flexural strength by approximately 1.7 times and flexural modulus by 2.8 times compared to the neat resin. This superior performance relative to TPO composites is attributed to robust interfacial hydrogen bonding among PBS carbonyl groups and the hydroxyl groups on the reed surface. Additionally, thermal and spectroscopic analyses revealed that these strong interactions elevate the crystallization temperature and generate a “Rigid Amorphous Phase” (RAF) that facilitates efficient stress transfer. These research findings demonstrate the feasibility of creating high-quality, bio-based composites, offering a sustainable method to reduce petroleum reliance and carbon dioxide emissions by upcycling musical waste. Full article

Anomaly identification and fault localization of wind turbines through Supervisory Control and Data Acquisition (SCADA) data is a popular topic today, but most studies overlook the complex time-space interdependence between wind turbine (WT) SCADA variables, which results in low detection accuracy for anomalies in critical moving components of the wind turbine. To address this problem, this paper proposes a fault detection and identification method based on a dynamic graph model with a causal spatio-temporal attention mechanism and variable-level normalized flow. First, it introduces a spatio-temporal attention mechanism under causality to extract the spatio-temporal attention mechanism under causality to extract spatio-temporal features of the variables and uses a graph convolutional neural network to represent the extracted spatio-temporal features as a dynamic graph. Secondly, a dynamic normalization flow is suggested for calculating the logarithmic density estimation between variables. Finally, the anomaly scores are calculated through logarithmic density estimation. Based on these scores, anomalies are detected and localized. Experimental validation on real SCADA data from wind turbines demonstrates that the method can effectively identify abnormal operating states and provide early warnings, achieving higher accuracy and greater stability. Full article

With the advancement of wind power technology towards larger-capacity and higher-power turbines, their main shaft bearings face significant lubrication challenges under extreme temperatures. In this study, seven modified greases were prepared by adding 0.5 wt.% of tungsten disulfide (WS₂), zinc sulfide (ZnS), and sulfurized isobutylene (T321). The concentration of all additives is given in weight percent (wt.%). Using a combined approach of friction and wear testing along with rheological analysis, this study systematically evaluated the tribological performance of the greases at high temperature (80 °C)—with the friction coefficient and wear scar diameter as key parameters—and their rheological properties across a wide temperature range (−20 °C to 80 °C), focusing primarily on shear stress and viscosity. All critical input parameters, including temperature, load, and shear rate, were precisely controlled and monitored using calibrated instruments. Results indicate that the WS₂ and T321 compounding system demonstrated optimal performance, achieving a low average coefficient of friction of 0.024 and an average wear scar diameter of only 0.367 mm. At the same time, the WS₂/T321 composite formulation exhibits excellent shear stability at high temperatures and good flow properties at low temperatures, demonstrating optimal viscosity–temperature characteristics. This study develops a promising grease formulation through multidimensional performance evaluation, offering key experimental support for designing high-performance wind turbine spindle bearing greases under high-temperature conditions. Full article

Renewable energy sources (RESs) play a key role in the global energy transition by reducing carbon emissions, enhancing energy security, and supporting sustainable development. This study presents a fairness-oriented energy management strategy for residential communities integrated with hydrogen-based technologies. The proposed system comprises photovoltaics (PV), a wind turbine (WT), an energy storage system (ESS), an electrolyzer (EL), a hydrogen tank, and bidirectional grid interaction. For the first time, four fairness indices are introduced to ensure the equitable utilization of renewable generation, stored hydrogen, and ESS among households. The problem was formulated as a mixed-integer linear programming (MILP) model to minimize operating costs. A case study conducted for a residential area in Lüleburgaz, Kırklareli assessed system performance in terms of cost, grid consumption, and carbon emissions. The results demonstrate that the proposed framework reduced grid consumption by 32.25% and carbon emissions by 31.82%. Moreover, increasing renewable capacity by 2.5 times reduced costs by 81,253.16 TL and yielded a profit of 70,107.39 TL, while a similar expansion of ESS capacity enabled 100% green energy accessibility for all households. Full article

In response to the need for cost-effective and resilient drivetrain architectures in renewable energy emulation platforms, this paper proposes a wind turbine emulator (WTE) designed to enhance the operational efficiency of variable-speed wind turbines (WTs) connected to electric generators in power grid applications. The proposed emulator relies on a robust sensorless vector-controlled induction motor (IM) drive fed by a reduced-switch soft–voltage source inverter (Soft-VSI) topology. The proposed control chain combines a second-order super-twisting sliding-mode flux observer, based on stator measurements, with a modified MRAS speed estimator whose Popov hyperstability offers explicit PI tuning and ensures stable sensorless speed convergence. The complete WTE design, from the aerodynamic model to the Soft-VSI induction motor drive, is implemented and evaluated in MATLAB/Simulink environment. A Mexican hat wind speed profile is used to excite the emulator and assess its dynamic behavior under diverse transient conditions. The simulation results demonstrate fast convergence of the estimated flux and speed, stable closed-loop operation when using the estimated speed, and strong robustness against no-loaded and loaded operations and rotor-resistance variations. Moreover, a comparative analysis between the proposed control scheme and a conventional first-order sliding-mode flux observer is carried out to highlight the enhanced flux and speed estimation accuracy, reduced chattering, and improved dynamic robustness of the WTE. The proposed framework provides a flexible tool to support the energy transition through the development of advanced wind energy system control strategies. Full article

The Atlantic City–South Pass (ACSP) orogenic gold district, Wind River Mountains, Wyoming, occurs in the Archean South Pass Greenstone Belt primarily within greywackes and igneous rocks metamorphosed to the upper greenschist–lower amphibolite facies. Approximately 10 Mt of gold has been produced from pyrite and arsenopyrite-bearing quartz veins in deformation zones at the brittle–ductile transition. Multiple generations of primary and/or pseudosecondary fluid inclusions in gold-bearing quartz veins include one- and two-phase gaseous CO₂-CH₄ ± N₂ inclusions and two- and three-phase gaseous CO₂-CH₄-H₂O inclusions with rare NaCl daughter minerals. These primary/pseudosecondary inclusions show a broad range of homogenization temperatures (T_h) of 177.2 to 420.0 °C, with salinities of halite-bearing inclusions of >26 wt. % NaCl, with a high concentration of CaCl₂. Secondary aqueous inclusions formed at lower values of T_h (80.9 to 243.4 °C, with one outlier of 301.1 °C). Carbon from graphitic schists associated with gold-quartz veins yields values of δ¹³C = −28.5 to −19.1 per mil, suggesting that the light C isotope compositions of some carbonates (δ¹³C = −11.0 to −1.5 per mil) involved exchange reactions with graphite in the schists. Isotopic compositions of sulfur in sulfides (δ³⁴S = −1.0 to 3.6 per mil), oxygen in vein quartz (δ¹⁸O = 7.36 to 10.38 per mil), and hydrogen in fluid inclusions in vein quartz (δD = −125 to −55 per mil) are permissive of both magmatic-hydrothermal and metamorphic dehydration models for the origin of gold mineralization. However, a potential source of magmatic–hydrothermal fluids, the post-metamorphic Louis Lake granodiorite was unlikely to transport gold in a vapor state to become focused into shear zones as previously proposed. We favor a metamorphic dehydration model in which gold was derived from the South Pass supracrustal sequence and deposited in second-order shear zones that are spatially related to the first-order Roundtop Mountain Deformation Zone. Full article

Wind turbine (WT) misalignment fault diagnosis is constrained by critical signal processing challenges: weak fault features, intense background noise, and poor generalization. This study proposes a lightweight method for high-precision fault diagnosis. A fixed-threshold wavelet denoising method with the scene-specific pre-optimized parameter a (0 < a ≤ 1.3) is proposed: the parameter a is determined via offline grid search using the feature retention rate (FRR) as the objective function for typical wind farm operating scenarios. A multi-scale depthwise separable CNN (MS-DSCNN) captures multi-scale spatial features via 3 × 1 and 5 × 1 kernels, reducing computational complexity by 73.4% versus standard CNNs. An attention-based minimal peephole LSTM (AttMPLSTM) enhances temporal feature measurement, using minimal peephole connections for long-term dependencies and channel attention to weight fault-relevant signals. Joint L1–L2 regularization mitigates overfitting and environmental interference, improving model robustness. Validated on a WT test bench, the Adams simulation dataset, and the CWRU benchmark, the model achieves a 90.2 ± 1.4% feature retention rate (FRR) in signal processing, an over 98% F1-score for fault classification, and over 99% accuracy. With 2.5 s single-epoch training and a 12.8 ± 0.5 ms single-sample inference time, the reduced parameters enable real-time deployment in embedded systems, advancing signal processing for rotating machinery fault diagnosis. Full article

An optimal topographical arrangement of wind turbines (WTs) is essential for increasing the total power production of a wind farm (WF). This work introduces PSO-GA, a newly formulated algorithm based on the hybrid of Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA) method, to provide the best possible and reliable WF layout (WFL) for enhanced output power. Because GA improves on PSO-found solutions while PSO investigates several regions; therefore, hybrid PSO-GA can effectively handle issues involving multiple local optima. In the first phase of the framework, PSO improves the original variables; in the second phase, the variables are changed for improved fitness. The goal function takes into account both the power production of the WF and the cost per power while analyzing the wake loss using the Jenson wake model. To evaluate the robustness of this strategy, three case studies are analyzed. The algorithm identifies the best possible position of turbines and strictly complies with industry-standard separation distances to prevent extreme wake interference. This comparative study on the past layout improvement process models demonstrates that the proposed hybrid algorithm enhanced performance with a significant power improvement of 0.03–0.04% and a 24–27.3% reduction in wake loss. The above findings indicate that the proposed PSO-GA can be better than the other innovative methods, especially in the aspects of quality and consistency of the solution. Full article

To improve the dynamic response and steady-state frequency quality of a wind–storage coordinated system during primary frequency regulation, and to address the secondary frequency dip caused by rotor kinetic energy recovery when a doubly fed induction generator (DFIG)-based wind turbine (DFIG-WT) participates in frequency support, this paper proposes a coordinated wind–storage primary frequency regulation strategy. This strategy synergistically controls the wind turbine’s rotor kinetic energy recovery and exploits the advantages of hybrid energy storage system (HESS). During the DFIG-WT control stage, an adaptive weighted model is developed for the inertial and droop power contributions of the DFIG-WT based on the available rotor kinetic energy, enabling a rational distribution of primary frequency regulation power. In the control segment of HESS, an adaptive complementary filtering frequency division strategy is proposed. This approach integrates an adaptive adjustment method based on state of charge (SOC) to control both the battery energy storage system (BESS) and supercapacitor (SC). Additionally, the BESS assists in completing the rotor kinetic energy recovery process. Through simulation experiments, the results demonstrate that under operating conditions of 9 m/s wind speed and a 30 MW step disturbance, the proposed adaptive weight integrated inertia control elevates the frequency nadir to 49.84 Hz and reduces the secondary frequency dip to 0.0035 Hz. Under the control strategy where wind and storage coordinated participate in frequency regulation and BESS assist in rotor kinetic energy recovery, secondary frequency dips were eliminated, with steady-state frequency rising to 49.941 Hz. The applicability of this strategy was further validated under higher wind speeds and larger disturbance conditions. Full article

The integration of high-penetration renewable energy sources (RESs) and electric vehicles (EVs) increases the risk of voltage fluctuations in distribution networks. Traditional static partitioning strategies struggle to handle the intermittency of wind turbine (WT) and photovoltaic (PV) generation, as well as the spatiotemporal randomness of EV loads. Furthermore, existing scheduling methods typically optimize EV active power or reactive compensation independently, missing opportunities for synergistic regulation. The main novelty of this paper lies in proposing a spatiotemporally coupled voltage-stability optimization framework. This framework, based on an hourly updated electrical distance matrix that accounts for RES uncertainty and EV spatiotemporal transfer characteristics, enables hourly dynamic network partitioning. Simultaneously, coordinated active–reactive optimization control of EVs is achieved by regulating the power factor angle of three-phase six-pulse bidirectional chargers. The framework is embedded within a hierarchical model predictive control (MPC) architecture, where the upper layer performs hourly dynamic partition updates and the lower layer executes a five-minute rolling dispatch for EVs. Simulations conducted on a modified IEEE 33-bus system demonstrate that, compared to uncoordinated charging, the proposed method reduces total daily network losses by 4991.3 kW, corresponding to a decrease of 3.9%. Furthermore, it markedly shrinks the low-voltage area and generally raises node voltages throughout the day. The method effectively enhances voltage uniformity, reduces network losses, and improves renewable energy accommodation capability. Full article