Search Results (944)

This paper proposes an annular suction caisson specifically designed to reinforce in-service monopiles and upgrade existing offshore wind farms to accommodate larger-capacity wind turbines. During penetration of the annular suction caisson into clay, the existing monopile restricts the inward migration of soil into the internal space of the caisson, promoting upward soil displacement and consequently increasing the height of the soil plug formed inside the caisson. In addition, the strain-softening behavior causes varying degrees of strength degradation in the clay along the caisson wall. The softened zones extend approximately one caisson wall thickness on the inner side and 1.2 times the wall thickness on the outer side of the caisson. Both effects should be considered for accurately predicting the penetration resistance of annular suction caissons. Therefore, three-dimensional large-deformation finite element analyses were performed to investigate the penetration behavior of annular suction caissons in strain-softening clay. A comprehensive parametric study was conducted to quantify the soil plug heave and overall penetration resistance. Meanwhile, the soil flow mechanism at the caisson tip, the evolution of clay strength along the caisson wall, and the formation characteristics of the internal soil plug were systematically examined. Based on the numerical results, a theoretical approach was developed to evaluate the penetration resistance of annular suction caissons. Full article

Accurate wind speed forecasting plays a crucial role in the safe and stable operation of wind farms and the efficient integration of renewable energy into modern power systems. However, wind speed exhibits complex spatiotemporal variations affected by diverse meteorological conditions, making high-precision prediction a long-standing bottleneck in wind power scheduling. This paper develops a U-shaped spatiotemporal feature fusion network named U-STNet, which realizes joint modeling of inter-turbine spatial correlations and multi-period long-range temporal dependencies. The model maps raw wind speed series to high-dimensional embeddings and adopts an encoder–decoder U-shaped architecture to complete feature encoding, reconstruction and multi-scale feature extraction, which effectively captures the inherent periodic and seasonal patterns of wind speed. Experiments on the SDWPF wind farm dataset show that U-STNet obtains competitive prediction accuracy across all multi-step forecasting horizons. Compared with traditional statistical models, recurrent neural networks and state-of-the-art Transformer baselines, the proposed method exhibits more stable error accumulation characteristics and superior long-step prediction performance. This verifies the effectiveness of jointly modeling turbine spatial topology and multi-scale temporal dependencies for wind speed forecasting. Full article

Fire-induced domino effects in tank farms can be catastrophic, particularly under wind conditions. However, due to multiple evolutionary stages, Computational Fluid Dynamics (CFD)-based modelling of wind-influenced, fire-induced domino effects and tank farm Time to Failure (TTF) calculation remain computationally expensive. This study addresses this gap by using Fire Dynamics Simulator (FDS) to model fire-induced domino effects in a tank farm and perform detailed tank farm TTF calculations across multiple wind speeds and primary pool fire scenarios. The FDS results showed that increasing wind speed from 0 to 8 m/s altered domino escalation, increasing incident heat flux on the downwind in-line tank by more than sevenfold (a 35% reduction in tank farm TTF). A new perturbation-based analytical formulation was then proposed for rapid determination of tank farm TTF under wind effects, without requiring complete CFD simulations of pool fire escalation. The formulation updates tank farm TTF under the no-wind baseline solution with wind-influenced perturbative correction terms. The proposed formulation agreed with the detailed CFD modelling-based calculation, with a mean relative error of 2.8% across all primary fire scenarios and wind conditions. This formulation provides a practical basis for rapid assessment of domino effects due to pool fire under wind conditions. However, it is calibrated for one specific six-tank configuration and crosswind directions and is not yet general. Full article

Owing to transportation, installation, grid-connection, and maintenance requirements, nearshore offshore wind farms are often located close to busy shipping routes, substantially increasing the risk of ship–offshore wind turbine (OWT) collisions. To enhance the impact resilience of OWT support structures against ship collisions, a novel honeycomb sandwich fender is proposed for tower protection. Nonlinear transient analyses were performed using ANSYS/LS-DYNA to simulate a 5000 t ship traveling at 2 m/s and colliding with a 4 MW OWT supported by a single-column tripod foundation. The effects of rubber and aluminum foam cores on the crashworthiness and protective performance of the fender were compared. The results show that the rubber core stores collision energy through recoverable large deformation and releases most of the stored energy during unloading, resulting in pronounced energy restitution and prolonged structural excitation. By contrast, the aluminum foam core dissipates 7.5 MJ through cell-wall buckling, progressive crushing, and plastic collapse, corresponding to 75% of the initial kinetic energy of the ship. Compared with the rubber-core fender, the higher initial stiffness of the aluminum foam increases the peak contact force by 23.1%, from 13.0 to 16.0 MN. However, its irreversible energy-dissipation mechanism reduces the maximum tower-top displacement by 40.0%, from 1.25 to 0.75 m, and decreases the residual tower stress after three successive collisions by 25.0%, from 200 to 150 MPa. These results demonstrate that, despite transmitting a higher peak contact force, the aluminum foam fender provides more effective overall protection under the collision conditions considered because of its greater irreversible energy-dissipation capacity. Full article

Following extreme disasters, hybrid wind farms comprising grid-following (GFL) and grid-forming (GFM) turbines can serve as black-start resources for power system restoration. However, wind power uncertainty, wake effects, and collection-grid restoration constraints complicate the startup sequence planning of GFL turbines within hybrid wind farms. To address these challenges, this paper proposes a multi-objective mixed-integer linear programming (MILP) model that jointly considers electrical impedance paths, wind uncertainty, and spatial wake effects. Information Gap Decision Theory (IGDT) is incorporated into active power support constraints to account for wind uncertainty through a robust adjustment of available generation capacity, while the Dijkstra algorithm is employed to convert collection-cable parameters into impedance-based cost factors for identifying minimum-impedance restoration paths and mitigating transient overvoltage risks. In addition, dynamic wake losses under non-uniform turbine layouts are quantified to capture the influence of startup sequences on local flow fields, and the resulting nonlinear terms are reformulated using the big-M linearization technique. Case studies considering different wind directions, time-varying wind speed conditions, and cable-impedance sensitivities demonstrate the effectiveness of the proposed framework. The results show that the proposed strategy provides a favorable balance between impedance-cost minimization, wake-effect mitigation, and robustness enhancement, while maintaining reliable restoration performance under diverse operating conditions. Full article

In response to stochastic fluctuations in large-scale wind power integration and the resulting peak-shaving challenges, high-precision forecasting with physical consistency is essential for grid safety. To address the inefficiency of physical models and poor interpretability of data-driven methods, this paper proposes a hybrid FCM-WGM-BiLSTM-Transformer (FW-BTP) framework integrating Fuzzy C-Means (FCM) clustering, Weighted Grey Model (WGM) trend extraction, and a coupled BiLSTM-Transformer module. Physical prior knowledge—wind turbine power curves—is embedded into the loss function via a Physics-Guided Loss Regularization (PGL) mechanism. Validated on measured data from a Xinjiang wind farm, the model achieves an $R^2$ of 0.9967, MAE of 6.11, and RMSE of 11.19. The proposed model reduces $R^2$ by 37% compared to the newer model KAN, and compared to the better-performing recurrent baseline model (BiLSTM, MAE = 8.75 MW), the proposed FW-BTP model reduces the MAE by 30% (to 6.12 MW). Ablation studies confirm the WGM reduces LogCosh loss from 9.57 to 5.12, and SHAP analysis verifies sensitivity to trend and physical wind speed features. The framework balances accuracy, robustness, and interpretability, supporting refined scheduling in modern power systems. Full article

Wind power integration into mountainous power grids amplifies probabilistic forecasting challenges arising from strong non-stationarity, multi-source meteorological redundancy and frequent curtailment events. To address the limitations of existing approaches, this paper proposes a multi-horizon probabilistic forecasting framework integrating multi-perspective entropy-weighted fusion, permutation-entropy-guided decomposition, and residual-anchored probability modelling. First, an Entropy-Weighted Multi-criteria Permutation Feature Importance (EW-MPFI) module fuses KSG mutual information, Tree-SHAP, and elastic-net permutation importance through entropy-based weighted aggregation, distilling 23-dimensional meteorological inputs into eight informative features while suppressing single-criterion bias. Then, a three-stage decomposition strategy applies ICEEMDAN primary decomposition, permutation-entropy and sample-entropy guided band reconstruction, and SSA secondary refinement on high-frequency components, achieving complexity-aligned multi-scale separation. Finally, a decomposition-aware patch-based Transformer backbone (DPC-Former) generates three-quantile point forecasts, upon which an NGBoost residual layer models the conditional distribution via natural-gradient optimization in the information-geometric parameter space. Case studies on a 130 MW mountainous wind farm in Sichuan, China, covering 8736 15-min samples with 566 curtailment samples (6.48% of the dataset), show that, under the partition-wise offline batch-evaluation protocol, the proposed framework achieves an NMAE of 5.21%, an NCRPS of 3.74%, and a PICP80 of 0.84 across forecasting horizons from 15 min to 4 h. Ablation analysis attributes NMAE improvements of 25.36% and 24.57% to the decomposition and feature-selection modules, respectively, while 50-seed ensembling further reduces NCRPS, NMAE, and NRMSE by 7.40%, 7.00%, and 13.70% relative to single-seed training. A fixed-checkpoint test-block diagnostic further shows limited sensitivity at approximately weekly and three-day decomposition cadences, but a material degradation at a one-day cadence. The reported metrics should therefore be interpreted as offline best-case results rather than as performance under strictly causal real-time deployment. Full article

This study investigates how offshore wind farm layout geometry influences farm-level performance using a computationally efficient Jensen–Park wake model combined with directionally resolved Weibull wind-speed statistics. A fixed-capacity 1.8 GW case study, consisting of 120 V236-15.0 MW turbines, is used to examine the effects of grid aspect ratio, inter-turbine spacing, cumulative row skew, and global layout rotation on wake losses, annual energy production (AEP), and capacity factor under representative offshore screening assumptions. Structured layouts with identical turbine count and installed capacity are compared with a regular baseline grid to isolate geometric effects within a consistent modelling framework. For the nominal offshore Jensen wake-expansion coefficient, k = 0.04, the highest sampled AEP is obtained for the 5 × 24 configuration, which produces 8930.69 GWh yr⁻¹ and a capacity factor of 56.64%. The regular baseline produces 7397.50 GWh yr⁻¹ and a capacity factor of 46.91%, corresponding to a 20.73% AEP increase for the highest sampled layout. However, the performance differences among Layouts D–F are small, indicating a high-performing layout plateau rather than a clearly separated optimum. The contribution of this paper is therefore not a new wake model, optimisation algorithm, or general offshore design rule. Instead, this study provides an auditable screening workflow that documents modelling assumptions, parameter bounds, coordinate transformations, convergence checks, sensitivity analyses, and spatial-efficiency indicators for one turbine model, one turbine count, one synthetic wind rose, and a limited set of structured row–column layouts. Full article

Methane (CH₄) emissions from dairy cattle contribute substantially to agricultural greenhouse gas production and are influenced by genetic, physiological, environmental and management-related factors. The present study investigated CH₄-related traits and genotype–system interactions in Holstein Friesian (HF) dairy cows using repeated laser methane detector (LMD)-based measurements. A total of 134 cows from one research herd reflecting a commercial production system were repeatedly recorded for CH₄ traits (739 observations per trait) between 2020 and 2024 and linked with milk performance test-day data, behavioral observations, environmental measurements and genomic breeding values. CH₄ traits were derived separately for respiration- and eructation-related emissions. Generalized linear mixed models revealed significant effects of wind speed, rumination behavior, interaction behavior and days in milk on several CH₄ traits. Across lactation, respiration-related CH₄ traits slightly decreased, whereas eructation-related traits increased toward later lactation stages. Correlations between CH₄-related breeding values and production traits were generally low to moderately negative, ranging from −0.24 to 0.08, indicating that selection for reduced CH₄ emissions may be achievable without major unfavorable effects on milk production traits. To evaluate the complex relationships among CH₄ emissions, production, behavior, environment, diet and genetic background, a structural equation model (SEM) was applied. Environmental conditions, particularly temperature and humidity, showed the strongest positive association with CH₄ emissions, while eructation-related CH₄ traits contributed more strongly to the latent CH₄ construct than respiration-related traits. Behavioral activity, especially rumination, indicated relevant associations with CH₄ expressions. The SEM further suggested that CH₄ emissions are shaped by interconnected environmental, physiological and genetic pathways rather than by a single dominant factor. Overall, the results highlight the importance of environmental sensitivity and longitudinal biological variation in CH₄ phenotypes under commercial dairy production conditions. Repeated on-farm CH₄ measurements, particularly eructation-associated traits, may provide valuable indicator traits for future genomic breeding and management strategies to reduce the environmental footprint of dairy cattle production. Full article

High-resolution wind resource maps are essential for wind farm siting and renewable-energy planning, but national-scale assessment is often limited by sparse meteorological stations and the coarse resolution of reanalysis products. This study introduces a self-supervised Physics-Constrained Super-Resolution CNN (PC-SRCNN) that enhances ERA5 wind fields from 0.25° to 0.025°, for settings lacking a high-resolution, hourly-resolved reference wind field, by embedding kinematic and vertical-profile consistency as soft regularization penalties, without solving the full Navier-Stokes momentum balance. The framework was evaluated against 190 independent INMET stations, alongside interpolation, data-driven, and climatological baselines, using a station-wise bootstrap and the Diebold-Mariano test. The full model reduced the bootstrap MAE from 1.22 to 1.04 m/s relative to native ERA5, a 14.85% improvement confirmed by both tests, and achieved a vertical-profile $R^2$ of 0.90 against baselines below 0.70. Ablation shows physical regularization is not automatically beneficial: a variant constrained only by horizontal kinematics performed significantly worse than ERA5, with gains obtained only when vertical logarithmic-profile consistency was included. All other baselines evaluated were statistically indistinguishable from native ERA5 against station observations. These results indicate that sharper spatial detail alone is insufficient to improve wind-resource estimates unless supported by physically meaningful, vertically consistent constraints. Full article

Wind farm layout optimization is commonly studied under a fixed number of turbines, although turbine quantity is closely coupled with wake effects and power generation efficiency. This study formulates the problem as a variable-turbine-number optimization task and systematically compares three representative variable-length approaches: dimension activation/deactivation, representation mapping, and variable-length evolutionary representation. The study also investigates structure-preserving search strategies based on binary occupancy representations. The findings suggest that, for variable-turbine-number wind farm layout optimization, the effective preservation and exploitation of spatial layout structures are more critical than merely handling dimensional changes. From a fitness-landscape perspective, structure-preserving representations appear to create more coherent neighborhoods and more accessible search topologies. These properties facilitate transitions across heterogeneous turbine-number subspaces and improve search performance. Full article

Wind power producers face increasing market risks in electricity spot markets because output uncertainty may lead to large imbalance penalties and unstable revenues. This study aims to improve the market participation performance of wind farm consortia by coordinating day-ahead bidding, real-time deviation correction, and internal revenue allocation. The main novelty of this study is the integration of consortium-level bidding, shared energy storage leasing, and post-settlement revenue-cost allocation within a unified decision-allocation framework. A two-stage coordinated bidding model is developed for a wind farm consortium that leases shared energy storage to mitigate real-time power deviations. A Shapley value-based allocation mechanism is further introduced to distribute consortium revenue, while the shared energy storage leasing cost is allocated using an additional revenue-proportional fairness rule. Case studies show that the proposed strategy can reduce deviation penalties, increase the final net revenue after leasing cost, and maintain fair incentives among consortium members. Sensitivity analyses further demonstrate that the economic performance of the consortium is affected by storage size, charging/discharging efficiency, and wind farm output correlation. The proposed framework provides a practical decision-making reference for wind power aggregation, shared energy storage utilization, and coordinated participation in electricity spot markets. Full article

Vertical-axis wind turbines (VAWTs) are recognized as a viable option for wind energy farms due to their compact design and suitability for different wind settings, such as urban and offshore environments. VAWT wind farms have been studied with respect to various turbine spacing and configurations that demonstrate that the VAWT wind farm is well-suited for improving efficiency while requiring less land, compared to horizontal-axis wind turbines (HAWTs). Moreover, the use of combined dual-rotor configurations has recently given attention as a passive strategy to enhance the aerodynamic performance of VAWTs. Despite these advances, the optimal arrangement of VAWT farms, including inter-turbine distances, clustering configurations, and land-use efficiency of such dual rotors, has yet to be explored. This study investigates different clustering scenarios, including vertically aligned pairs and staggered clusters of three turbines, to evaluate their impact on power capture and land usage for a dual-rotor straight-blade vertical-axis wind turbine (DR-SBVAWT). The 2D-dimensional transient (URANS) numerical simulations are conducted using the k-$\omega$ SST turbulence model. Performance indices, namely, total power coefficient and improvement relative to standalone turbines, are analyzed. Wake effects are investigated through detailed velocity contour plots of the wind field. Results reveal that a DR-SBVAWT turbine arrangement can enhance wind farm performance by approximately 25% for two turbines and about 20% for three staggered turbines, with required spacing of 1.5 D and 2.5–3 D, respectively (Here, D is the outer diameter of the DR-SBVAWT). The study overall provides insights into the optimal placement and configuration of DR-SBVAWTs for maximizing energy output while minimizing land usage, offering guidance for the design of more efficient VAWT farms. Full article

Wind turbine gearbox degradation driven by lubricating oil contamination represents one of the most environmentally and economically consequential challenges facing modern wind energy operations. This study proposes a dual-reservoir imperfect preventive maintenance strategy designed to extend gear train service life, reduce the carbon footprint of maintenance operations, and recover renewable energy production losses inherent to conventional intervention practices. The proposed architecture employs two alternating oil reservoirs. While one supplies the active lubrication circuit at full turbine output, the second undergoes filtration, completely decoupling the filtration operation from production continuity. When the concentration of metallic particles resulting from gear wear exceeds a predefined contamination threshold in the lubricating oil, imperfect preventive maintenance (IPM), performed in parallel with an oil change operation, is initiated; this action partially restores the gear train failure rate to an intermediate value between the degraded and as-new states. A mathematical model is derived to jointly optimize the filtration interval TF and the preventive maintenance interval TM, minimizing the average total cost per unit time over a finite operational horizon while explicitly incorporating environmental costs attributable to each filtration cycle. Numerical optimization yields the optimal filtration interval TF and preventive maintenance interval TM that minimize the total average cost per unit time over the operational horizon H. A dedicated environmental performance assessment demonstrates that the proposed strategy substantially recovers lost wind power generation, significantly reduces hazardous lubricating oil waste and lowers total CO₂-equivalent emissions. This confirms the strategy’s meaningful contribution to sustainable wind energy operations. Sensitivity analyses confirm the robustness of the optimal solution across varying operational and economic conditions, providing wind farm operators with an adaptable and environmentally responsible decision-making framework. Full article

Wind power forecasting plays a crucial role in power dispatch and safety management of wind farms. However, the insufficient integration of multimodal heterogeneous data and the limitations of conventional graph construction strategies significantly restrict forecasting performance. Existing approaches either rely on simple feature aggregation, which cannot fully capture cross-modal dependencies, or adopt predefined or single-criterion graph construction methods that fail to characterize complex turbine relationships involving spatial, temporal, and nonlinear correlations. To address these challenges, this paper proposes a Multimodal Adaptive Fusion Graph Neural Network (MAF-GNN) for short-term wind power forecasting. First, a Modality-Aware Representation Learning (MARL) module is developed to extract informative multimodal representations by modeling modality-specific characteristics and cross-modal dependencies through attention-based fusion. Second, an Adaptive Graph Learning with Multi-Similarity (AGL-MS) module is introduced to parametrically integrate four complementary similarity priors—geographic distance, Dynamic Time Warping (DTW), Maximal Information Coefficient (MIC), and cosine similarity—for adaptive turbine correlation graph construction. Furthermore, a Credibility-Modulated Graph Convolutional Network (CM-GCN) is developed to reduce the influence of unreliable node information during message propagation. Extensive experiments conducted on the SDWPF dataset demonstrate that MAF-GNN reduces MAE by 14.0–21.3% compared with sequential baselines and achieves 5.3–10.2% improvement over spatiotemporal graph-based models. Ablation studies further verify the complementary effectiveness of each proposed module in improving forecasting performance. Full article