Search Results (2,776)

Stability and sustainability are the operational bottom lines of modern power grids. However, the inherent volatility and non-stationarity of wind energy, particularly in complex terrains, severely threaten power grid stability. To address this challenge, we propose an end-to-end architecture named the Adaptive Multi-scale Routing Wind Power forecasting (AMR-Wind) framework. The framework is principally composed of three sequential modules: an Adaptive Frequency Disentanglement Module (AFDM), an inverted Transformer (iTransformer), and a Scale-Routing Gated Recurrent Unit (SRGRU). The AFDM utilizes a differentiable filter bank to dynamically disentangle complex spectral signatures and mitigate mode mixing. The iTransformer is employed to effectively capture the complex multivariate dependencies between these disentangled modes and exogenous meteorological features. The SRGRU utilizes hierarchical temporal routing to synchronize localized high-frequency ramp events with macroscopic evolutionary trends. Comprehensive evaluations across four diverse wind farms demonstrate that AMR-Wind reduces the RMSE by an average of 8.4% and improves the $R^2$ by at least 1.0% compared to state-of-the-art baselines. Ablation studies further confirm the modules’ strong synergistic effects, yielding a 7.6% reduction in forecasting errors. This framework reduces the error in wind energy prediction, providing a reliable tool for the stability and sustainability of the power grid. Full article

To evaluate the potential biological effects of electromagnetic fields from offshore wind farms on Sebastes schlegelii, a laboratory-controlled chronic exposure experiment was conducted using a magnet-based static magnetic field system. Each group contained 60 fish distributed across four replicate tanks, with 15 fish per tank, and the fish were continuously exposed for 20 d under controlled water-quality conditions. Daily video monitoring of collective shoaling behavior was combined with multi-tissue physiological and biochemical analyses. Electromagnetic field exposure increased the swimming speed, burst frequency, activity ratio, spatial coverage, occupancy entropy, and polarization, while reducing the nearest neighbor distance, group radius, and group area. At the physiological level, cortisol increased mainly in the liver and brain, ACTH showed tissue-dependent modulation, SOD remained relatively stable, and glutathione increased in multiple tissues, especially in the liver, gut, and brain. Correlation analysis indicated a close coupling between behavioral reorganization and endocrine–redox regulation, suggesting that chronic EMF exposure shifted Sebastes schlegelii into a stress-associated but functionally coordinated collective state. Full article

As wind farms in North America near the end of their design life, different end-of-life options need to be considered. Common options include decommissioning, lifetime extension, and repowering. In this research, a methodology to support early-stage repowering decisions is presented. Performance decline and repowering forecasts are obtained by combining analysis of past performance data and preliminary site plans for new turbines with turbine performance models from windPRO software. Financial metrics are computed using a simple techno-economic model with parameters informed by historical financial records. Repowering decisions are often sensitive to assumptions on key parameters, such as capital cost of repowering, which are poorly defined at the beginning of the process and subject to change quickly. This makes it difficult to provide guidance that will remain relevant as more information is obtained during future project planning stages. In this work, constrained optimization methods are used to identify sets of the key inputs that lie on the break-even point at which repowering is more profitable than continuing operation. Using this approach, which is novel in this context, the client gains an intuition for the ‘envelope’ within which the recommended guidance still holds. This decision-making process is applied to a case study using performance data and cost ranges from a real, anonymous wind farm. Full article

The inherent intermittency and uncertainty of wind power generation pose significant challenges to grid security and the integration of renewable energy. Accurate and reliable short-term wind power forecasting is crucial for enhancing wind energy usage and ensuring the safe operation of power systems. Current mainstream forecasting methods inadequately model spatial correlations among regional wind farms. Additionally, wind power generation is susceptible to sudden changes in weather conditions and environmental factors, limiting the robustness of existing forecasting methods when confronting dynamically changing prediction environments. This poses major challenges for accurate and reliable regional wind power forecasting. This paper employs Graph Convolutional Networks (GCN) to model spatial connections between wind farms while introducing a combined TCN-Transformer model for temporal feature extraction and dependency modeling. Furthermore, to enhance prediction accuracy and reliability, Deep Q-Network (DQN) is incorporated to dynamically correct model prediction errors. Experimental results demonstrate that the proposed short-term wind power forecasting method achieves an RMSE of 60.14 and an MAE of 45.98, showing significant improvement over predictions from models without DQN error correction and other comparative models. Future work may extend the forecasting horizon to provide more information support for grid supply security decisions. Full article

Accurate forecasting of wind and photovoltaic power remains challenging due to the strong nonlinearity, nonstationarity, and seasonal heterogeneity of renewable generation series. To address this issue, this study proposes a hybrid forecasting framework integrating time–frequency joint analysis (TFAA), temporal convolutional networks (TCN), long short-term memory (LSTM), and multi-head self-attention (MHSA). Wavelet transform is used to extract frequency-domain representations, which are jointly encoded with the original time-domain sequence through a dual-branch architecture and adaptively fused. The fused features are then processed by a TCN-LSTM backbone to capture both long-range dependencies and short-term dynamics, while MHSA is introduced to enhance global contextual modeling. Experiments on wind-farm and photovoltaic datasets from China, together with external validation on the NREL WIND Toolkit and the GEFCom2014 Solar benchmark, show that the proposed model achieves the best overall seasonal performance and maintains competitive improvements on public benchmarks. Additional ablation studies, repeated-run statistical validation, persistence-based skill-score analysis, prediction-interval evaluation, ramp-event assessment, meteorological-driver enrichment, permutation-based driver attribution, regime-conditioned error diagnostics, and transferability evidence analysis further confirm the effectiveness, robustness, physical consistency, and practical applicability of the proposed framework. The results indicate that the proposed model provides a reliable and operationally relevant solution for short-term wind and photovoltaic power forecasting. These findings further support sustainable renewable-energy integration, smart-grid dispatch, and low-carbon power-system operation. Full article

This article proposes and validates a hybrid structural reinforcement strategy for onshore wind turbine foundations in repowering projects, enabling the installation of higher-capacity units without demolishing the existing foundation. In a context of increasing demand for renewable energy and infrastructure optimization, the original foundation is reused as the primary element for global stability and serviceability limit state (SLS) requirements, while ultimate limit state (ULS) demands, arising from the replacement of approximately 1.5 MW turbines with 4.1 MW and 6.25 MW units with power ratings representative of various manufacturers’ models in the current market are resisted by a new peripheral reinforced concrete strengthening system. The study considers both shallow (gravity) and piled foundation typologies, which are the most common globally for wind turbines. This solution, applied to a commercially operating wind farm in southern Brazil with actual load data, demonstrated a substantial reduction in concrete volume–up to 80% for shallow foundations and 40% for piled foundations compared to constructing an entirely new foundation. Structural assessment was performed through numerical modeling in SAP2000, employing a shell-beam hybrid model validated against a 3D solid reference, combined with analytical verifications of limit states. Results confirm that the proposed solution ensures global serviceability and adequate ultimate limit state capacity, achieving significant material optimization. This offers a sustainable and efficient alternative for repowering wind turbine foundations, with notable economic and environmental benefits, including the elimination of demolition, transportation, and material disposal costs. Full article

This paper studies the dynamic behavior of a catamaran hovercraft wind farm service vessel (CHWFSV) during the berthing coupling process with a wind turbine tower, aiming to enhance its safety and reliability in engineering applications. By constructing an arc-shaped elastic fender and employing computational fluid dynamics (CFD), it investigates the motion response under transverse waves considering the effects of thrust, air-cushion flow and the elasticity coefficient of the fender. A finite element analysis (FEA) model of the arc-shaped fender, accounting for elastic stress and strain, is developed to study its coupled mechanical behavior under different thrust conditions. The research in this paper is based on numerical CFD simulation with experimental validation. The motion modeling under transverse waves is further verified through uncertainty analysis. The series of research results indicate the following: vessel rolling resonance occurs at λ/L = 1.667 (λ/L denotes the dimensionless wavelength-to-length ratio); increasing air-cushion flow extends the roll period and reduces roll amplitude at λ/L = 0.667, while applying thrust at λ/L = 1.667~3 lowers roll but reduces pitch and heave stability; relatively good berthing performance is achieved when F_CM/∆ = 0.054 and the elastic coefficient is 1.25 × 10⁷ Pa/m (Δ represents the vessel weight). Full article

To mitigate the volatility and instability caused by large-scale wind power integration in new-type power systems, hybrid energy storage systems (HESSs) can offer effective frequency support to wind farms. This paper presents a coordinated wind storage frequency control strategy that incorporates optimal power allocation within an HESS. First, wind power output is decomposed and reconstructed into low- and high-frequency components via variational mode decomposition (VMD) optimized with the multi-verse optimization (MVO) algorithm, followed by the establishment of a PI-based HESS frequency response model. Second, an SOC-aware flexible frequency division strategy is designed by coordinating the participation sequence of the wind turbine and the HESS. The regulation process is divided into three stages, namely, wind turbine regulation, joint wind storage regulation, and HESS-dominant regulation, to suppress frequency fluctuations induced by wind power variations. Finally, primary frequency regulation performance indices are proposed and validated in a three-machine, nine-bus system. The simulation results demonstrate that the coordinated use of different storage types within the HESS enhances the grid-connected stability of the wind storage system, while the incorporation of hybrid storage improves wind power utilization. Full article

In this paper, a short-term active power curtailment (ST-APC) strategy for doubly-fed induction generator (DFIG) wind farms is proposed to enhance first-swing rotor angle stability under fault disturbances. While wind power is a clean renewable resource that is widely deployed, its large-scale integration heightens concerns about transient stability. After analyzing DFIG operating principles, this study advocates for using short-horizon active power control to mitigate the adverse stability impacts of wind farms. Using the Western System Coordinating Council (WSCC) three-machine nine-bus test system, the effectiveness of the ST-APC strategy across diverse operating conditions was verified. This study is based on the fundamental principle that reducing the output of wind turbines is required for first-swing stability after faults to increase the kinetic energy of synchronous machines. A closed-loop control strategy combining voltage drop, frequency change, and a timer is designed. The correlation laws between various control parameters such as control activation timing, duration, and modulation depth and first-swing stability are analyzed, providing references for parameter selection in engineering applications. The findings indicate that the proposed strategy is practical and adaptable, making it suitable for power systems with high wind power penetration. Full article

Strategic bidding for wind–battery hybrid systems is increasingly critical as electricity spot markets transition toward market-oriented mechanisms, particularly in Chinese pilot regions. However, dual uncertainties—wind generation variability and volatile locational marginal prices (LMPs)—expose market participants to significant financial tail risk. This study develops a risk-constrained reinforcement learning framework for optimal bidding of wind–storage hybrid systems. We employ soft actor–critic (SAC) for continuous action control and integrate conditional value-at-risk (CVaR) into reward design to explicitly penalize low-probability, high-loss outcomes. The framework incorporates realistic operational constraints, including linearized battery degradation costs and a market-compatible single-bid abstraction for hourly settlement. Using one-year historical operational data from a 150 MW wind farm (with a 91-day test period), we find that storage integration increases annual profit by 108.4–114.2% relative to wind-only operation. Critically, the SAC–CVaR policy (η = 0.35) preserves 97.3% of risk-neutral profit ($7.71 M vs. $7.93 M) while substantially mitigating downside risk: CVaR@95% improves by 42.4% (−$549 vs. −$952) and VaR@95% improves by 30.1% (−$275 vs. −$393). The trained policy achieves sub-millisecond inference (0.262 ms per decision, ~3820 decisions/s), corresponding to a 3.8 × 10⁴–5.7 × 10⁴× speedup over optimization-based solvers (10–15 s per decision), enabling real-time deployment. Behavioral analysis reveals that the agent learns adaptive, forecast-normalized bidding strategies with more conservative reporting in high-price regimes and counter-cyclical battery dispatch patterns, demonstrating effective coordination between profitability and risk control under volatile market conditions. Full article

Accurate wind power forecasting is essential for efficient wind farm operation and reliable grid dispatch. This study proposes a site-adaptive forecasting framework that integrates machine learning, Tree-structured Parzen Estimator (TPE)-based Bayesian hyperparameter optimization, and SHapley Additive exPlanations (SHAP) for interpretability. Using real-world meteorological and power generation data from two wind farms, we first perform joint-distribution feature analysis to characterize statistical relationships between key inputs and power output, supporting model development and interpretation. TPE optimization is then applied to six benchmark models (CatBoost, Extra Trees, GBM, LightGBM, TabNet, and XGBoost). The optimized Extra Trees model achieves the best performance at Site 1 (R² = 0.965, RMSE = 3.872 kW, MAE = 2.333 kW), whereas the optimized XGBoost model performs best at Site 2 (R² = 0.921, RMSE = 3.049 kW, MAE = 1.382 kW), demonstrating the effectiveness of TPE tuning and the strong predictive capability of tree-ensemble learners. SHAP analysis further reveals heterogeneous drivers across sites: Site 1 benefits from synergistic wind-speed contributions across multiple heights, while Site 2 is primarily governed by hub-height wind speed. Overall, the proposed framework achieves both high accuracy and robust interpretability for multi-site wind power forecasting. Full article

This wind farm study provides a detailed and deep investigation into numerous aspects of both wind dynamics and the associated wind turbine performance via a wind data analysis utilizing an extrapolated timeframe of 50 years. The major wind characteristics assessed included wind speed and direction, flow inclination, turbulence intensity, and wind speed (average based on extremes) over the entire duration of the evaluated data set. A majority of study results indicated only narrow wind speed ranges (6.3 m/s to 7.0 m/s) for turbine operation within the wind farm. Higher turbine operation speeds than the average measured wind speed may significantly increase turbine energy output. Turbines were evaluated across numerous geographic locations, resulting in average flow inclination (−4.12° to 1.57°) from the vertical to horizontal directions. The variation in flow inclination indicates that there is a geographic component that likely creates a localized terrain impact on turbine performance. Similarly, the measurement of turbulence intensity was also assessed, which indicated elevated levels of turbine mechanical stress and additional requirements for turbine maintenance. Energy production analyses from each turbine in the wind farm exhibited various regions of energy loss, with the highest energy losses associated with select turbines. Full article

Wind energy stands as one of the most technologically mature renewable sources, playing a pivotal role in the mitigation of greenhouse gas emissions. However, wind farms and associated infrastructures increase collision risk for flying organisms. Implementing higher cut-in speeds is a proven mitigation strategy to significantly decrease wildlife mortality rates, particularly for bat species, by preventing turbine operation during low-wind periods of high activity. The suggested, non-standard, increased cut-in speed for wind turbines is generally 5.0 m/s. To test the effectiveness of cut-in speed increase, bat activity was monitored at three wind farms in northern Portugal (Gevancas, Azinheira, and Lagoa de Dom João e Feirão), to characterize spatial and temporal activity patterns and assess the potential associated risk. Ultrasonic acoustic detection was carried out at fixed stations, at heights of 55 m above ground level from March to October. Wind speed data were recorded concurrently using anemometers mounted on meteorological towers. Contradicting recommendations, the results show that significant bat activity might occur at wind speeds above the current curtailment values. Since turbine operation coincides with peak bat activity, it is imperative to implement site-specific mitigation strategies, such as optimized cut-in speeds, to minimize mortality risk. Full article

This study presents a structural and geotechnical assessment of an onshore wind turbine foundation that has been in service for approximately 15 years. It aimed to evaluate its suitability for service life extension under the current operational conditions, within the broader context of decision-making in aging wind farms. The investigation integrated original design documentation, detailed field inspections, in situ and laboratory geotechnical testing, and advanced 3D numerical modeling incorporating soil–structure interaction effects. Verification procedures followed international standards and current guidelines for the design and reassessment of wind turbine foundations. Critical structural and geotechnical aspects, including internal forces and reinforcement demand, stiffness, bearing resistance, settlement, and global stability, are examined to verify performance under the current operational loading conditions. The results provide a sound technical basis for strategic decision-making regarding service life extension or decommissioning of wind turbines in established wind farms, and constitute an essential baseline for any future structural upgrading associated with repowering strategies. Full article

Vertical-axis wind turbine (VAWT) clusters have been extensively investigated owing to their positive aerodynamic interactions. However, accurate predictions of the flow field and power output of each rotor in VAWT clusters using high-fidelity computational fluid dynamics (CFD) remain computationally expensive. In this study, we propose a fast computation method for the flow field and operating state of each rotor of VAWT clusters using temporally and spatially averaged velocity data compressed from an unsteady velocity field obtained via a 3D-CFD simulation of an isolated rotor. First, the unsteady 3D flow field in the 3D-CFD simulation is time-averaged over several revolutions. Next, the temporally averaged velocity is spatially averaged in the vertical direction to obtain spatially compressed data. Based on a previously developed fast computation framework, a wind-farm flow field is constructed using condensed two-dimensional velocity data obtained from a single turbine. The proposed method is applied to three-rotor configurations, and the rotational speeds of the turbines are compared with the wind-tunnel measurements. The results show that the proposed method substantially improved the prediction accuracy while maintaining a low computational cost. In addition, it can be used to efficiently design and optimize turbine layouts in VAWT wind farms. Full article