Search Results (1,719)

The supervisory control and data acquisition (SCADA) data are increasingly used for wind turbine anomaly detection, but purely data-driven methods may be limited by weak physical interpretability, class imbalance, and reduced generalization under changing wind-farm operating conditions. Although the Extreme Gradient Boosting (XGBoost) is effective for structured nonlinear classification, its use in SCADA-based anomaly detection remains affected by label quality, probability calibration, and cross-farm transferability. This paper validates a physics-informed XGBoost decision layer using residual-based indicators, including power-curve residuals, gearbox and generator thermal residuals, rotor-speed variance, active-power ratio, and wind-speed fluctuation. Comprehensive Anomaly Detection Benchmark for Wind Turbine SCADA Data (CARE) logbook labels are used as the reference labels, while 2σ, 3σ, and 4σ residual thresholds are evaluated as competing rule-based detectors. The decision layer is trained and internally tested using event-grouped chronological splits from Wind Farm A and externally evaluated on unseen Wind Farms B and C. The results show physically interpretable anomaly detection behavior, although performance varies across validation settings. Under external Farm A to Farm B/C transfer, XGBoost achieved row-level F1-scores of 0.6296 and 0.6551, respectively. Shapley additive explanations (SHAPs) link anomaly predictions mainly to thermal, power-conversion, and operating-context features. The findings support the proposed decision layer as an interpretable benchmark-validation framework, while showing that additional maintenance-log validation is required before definitive component-level fault-diagnosis claims can be made. Full article

In modern power systems, the rapid growth of renewable energy capacity, such as wind and solar photovoltaic (PV) power, has led to a decline in system equivalent inertia and primary frequency regulation margin. At the same time, net load fluctuations have intensified across multiple time scales, making it more likely for the RoCoF, frequency nadir, and quasi-steady-state frequency deviation to approach safety limits following disturbances. To achieve a balance between frequency security and economic operation, this paper proposes a two-stage stochastic frequency-security-constrained unit commitment (FSC-SUC) model tailored for scenarios with high renewable energy penetration. The day-ahead hourly dispatch stage jointly determines the on/off status and reference output of synchronous units and the reservation of slow frequency regulation capacity, as well as energy storage charging and discharging plans, SoC trajectories, and the reservation of fast frequency regulation capacity. The intraday minute-level real-time dispatch stage accommodates prediction errors through scenario-based rescheduling and ensures the deliverability of both slow and fast frequency regulation capabilities via commitment consistency constraints. To address the challenge of directly embedding frequency nadir constraints into mixed-integer optimization, this paper employs a modeling approach that combines analytical criteria with linear surrogate constraints. The RoCoF and quasi-steady-state frequency deviation are specified via aggregated analytical constraints, while the nadir is embedded into the main problem after generating samples offline using a simplified frequency response model and training a polyhedral linear surrogate for external approximation. The safety margin is then calibrated using high-quantile residuals from the validation set to ensure conservativeness. Case studies on the IEEE 33-bus system under different renewable penetration levels demonstrate that the proposed method significantly reduces the probability of frequency nadir violations and load-loss risk with only a modest cost increase while also improving coordination between fast and slow frequency regulation. Full article

Rapid changes in meteorological conditions can lead to frequent switching of wind turbine operating states, causing wind power sequences to exhibit pronounced non-stationarity and multimodal characteristics. As a result, conventional single prediction models often struggle to simultaneously maintain forecasting accuracy and stability under different operating conditions. To address this issue, this paper proposes a wind power forecasting method based on the Convolutional Normalized Transformer Encoder and Multi-Task Learning (CNTE-MTL). First, operating samples of wind turbines are divided into different operating conditions according to typical meteorological variables, such as wind speed, wind direction, and ambient temperature, to characterize differences in meteorology-driven operating patterns. Then, wind power forecasting under different meteorological conditions is formulated as multiple related subtasks, and a multi-task learning framework consisting of a shared feature extraction network and condition-specific prediction heads is constructed. In this framework, the shared feature extraction network employs one-dimensional convolution to extract local temporal fluctuation information and combines it with a Transformer encoder to capture long-term dependency features. The condition-specific prediction heads further characterize the differentiated power evolution patterns under different meteorological conditions, thereby enabling the sharing of common cross-condition information and differentiated modeling. Short-term forecasting, long-term forecasting, supplementary comparative experiments, and ablation experiments are conducted based on SCADA data from an actual wind farm. The results show that the proposed CNTE-MTL model achieves an RMSE of 0.0165 and an $R^2$ of 0.9689 in the one-month short-term forecasting experiment, and an RMSE of 0.0072 and an $R^2$ of 0.9980 in the three-month long-term forecasting experiment, outperforming comparative models such as CNTE, Informer, Transformer, TCN, and LSTM. The ablation experiments further verify the effectiveness of meteorology-driven operating condition division, the shared feature extraction network, and the condition-specific prediction heads in improving forecasting performance. Full article

Wind power systems operating in networked environments are vulnerable to stochastic disturbances, measurement noise, model mismatch and false data injection (FDI) attacks. These uncertainties may corrupt feedback information and degrade closed-loop control performance. This paper proposes an integrated extended Kalman filter (EKF)-based resilient Lyapunov model predictive control (RLMPC) framework for the secure control of wind power systems under bounded stochastic FDI attacks. A residual-based chi-square (${\chi }^2$) detector is embedded into the EKF update to evaluate the credibility of received measurements, and the resulting attack-aware state estimate is applied to the RLMPC controller at each sampling instant, constructing an EKF-RLMPC strategy. The proposed EKF-RLMPC scheme therefore links attack detection, state estimation, and predictive control within a unified secure-control framework for wind power systems. It is proved that the posterior estimation error remains bounded and that the closed-loop state is ultimately bounded under the proposed EKF-RLMPC scheme. Simulation studies under different FDI attack probabilities show that the proposed method improves state-estimation accuracy and control performance. Full article

The operational integration of renewable energy into contemporary power systems requires accurate and dependable wind power forecasting, particularly in multi-site settings with nonlinear temporal dynamics, inter-site dependence, and forecast uncertainty. Static site conditioning, conditional variable selection, dynamic graph learning, encoder–decoder temporal fusion, interpretable temporal attention, quantile regression, and post hoc split conformal calibration are all combined in this work to create DG-TFT-CQR, a global multi-site historical-power-based probabilistic forecasting framework. A representative eight-site subset of the AEMO 5 Minute Wind Power benchmark was used to evaluate the model under four different forecasting settings: H1, H3, H6, and H12. The proposed model demonstrated the most balanced probabilistic behavior and the strongest overall point-forecasting performance over these horizons among the compared baselines. The MAE/RMSE/R² values for the point-forecasting results were 0.025490/0.043186/0.980096 at H1, 0.037241/0.062569/0.958221 at H3, 0.047917/0.079747/0.932133 at H6, and 0.062891/0.102751/0.887340 at H12. Additionally, the model preserved competitive interval sharpness while maintaining empirical coverage near the nominal 90% target. DG-TFT-CQR is the most robust balanced framework, with particularly evident advantages at H1 and H12, according to ablation, site-wise, daily case, statistical, and complexity analyses. In pairwise comparisons, H3 and H6 correspond to more mixed regimes. All things considered, the suggested approach offers a reliable and practically significant solution for multi-site wind power forecasting that takes uncertainty into account. Full article

Long-term energy system planning depends on projections of future wind turbine characteristics, yet existing approaches rely on either costly expert elicitation or deterministic trend extrapolation without formal uncertainty quantification. We present a Bayesian logistic framework that models the temporal evolution of hub height, rotor diameter, and specific power as physically constrained growth and decay processes, producing full posterior predictive distributions via Markov Chain Monte Carlo sampling. The framework is validated across three major onshore wind markets: Austria (534 turbines, 2000–2025), Germany (31,202 turbines, 1988–2026), and the United States (71,457 turbines, 1986–2025); spanning different market structures, regulatory environments, and data availability. Systematic benchmarking against linear, polynomial, and maximum-likelihood alternatives demonstrates superior hindcast performance, particularly for long-range projections where physical saturation constraints become relevant. Prior sensitivity analysis reveals that posteriors are robust for data-rich regions but honestly reflect prior influence for small datasets, identifying where expert knowledge is essential. We extend the framework to climate-aware energy yield estimation by propagating turbine posteriors through synthetic power curves and site-specific wind resource projections under SSP2-4.5 and SSP5-8.5, decomposing the total uncertainty into technology and climate components. When climate uncertainty is measured by scenario spread alone, technology uncertainty dominates. However, accounting for the full inter-model spread across 13 CMIP6 global climate models reveals that climate uncertainty becomes substantial (14–56%) and region-dependent, underscoring that both sources require explicit quantification. The open-source pipeline is designed for direct adoption in energy system planning workflows. Full article

Accurate wind-power forecasting is essential for grid scheduling when renewable generation becomes highly variable. This study developed WindPower-SAFusion, an Informer-inspired forecasting model designed for long wind-power sequences. The framework is built around three complementary designs. First, ProbSparse self-attention is used to lower the attention cost from $O\left(L^2\right)$ to $O\left(L\log L\right)$ while retaining informative temporal dependencies. Second, convolutional distillation is embedded in the encoder to summarize local fluctuations and form multi-scale representations. Third, historical theoretical power and wind speed are fused in a recursive forecasting scheme for multi-step prediction. The model is evaluated using measured data from the Daliang Wind Farm in Guazhou, Gansu Province, China. Experiments conducted using 1-day, 3-day, and 7-day horizons show that WindPower-SAFusion obtained lower errors and higher explanatory ability than the selected statistical and deep learning baselines. The ablation results further confirm the contributions of sparse attention, convolutional feed-forward extraction, and sequence distillation. These findings indicate that the proposed framework can provide an effective data-driven tool for wind-farm dispatching and power-management applications. Full article

High spatial resolution wind speed data is very important for wind farm planning, design, operation and maintenance. But due to cost, site and other factors, it is impossible to build a large number of anemometer towers to obtain high spatial resolution measured data. Therefore, this paper proposes a method for generating wind speed data in renewable energy bases based on physics-informed neural networks, which incorporates fluid mechanics control equations such as the Navier−Stokes equation as physical constraints into the model training process. The model’s input includes the wind speed data and the wind direction data of the anemometer towers as input, as well as the geographical difference data between the input anemometer towers and the output point, enabling to learn the mapping relationship between geographical differences and wind speed differences at different locations, achieving the goal of generating high spatial resolution wind speed data. Using normalized root mean absolute error (NMAE) to measure the model error, the average wind speed error and the average wind direction error of the proposed wind speed data generation method on different test sets are 8.28% and 10.50%, which is lower than that of BP neural network and graph convolutional neural network, and can provide more refined data support for wind turbine layout planning and wind farm power prediction of renewable energy bases. Full article

Accurate forecasting of offshore wind power is essential for the stability of power systems, yet it remains challenging due to the strong non-stationarity and complex multivariate coupling of meteorological data. To address the tendency of error accumulation in medium- and long-term predictions, this paper proposes a novel framework, termed ISSAVMD-TCN-SOFTS, which integrates adaptive signal decomposition with lightweight deep temporal modeling. Specifically, an improved sparrow search algorithm, enhanced by Lévy flight and sine–cosine modulation mechanisms, is introduced to adaptively optimize the parameters of variational mode decomposition (VMD). This optimization ensures the robust decomposition of highly non-stationary power series. Furthermore, the framework combines the capability of temporal convolutional networks (TCN) to extract multiscale local temporal features with the efficiency of the STAR module in SOFTS for modeling global channel dependencies. Experiments on multi-site, multi-horizon SCADA data from real offshore wind farms show that the proposed model reduces MAE and RMSE by 10–45% compared with mainstream linear models, recurrent neural networks, and Transformer-based models, and maintains high stability over extended forecasting horizons. The results confirm that the integration of adaptive decomposition and collaborative temporal-channel modeling provides an effective solution for the accurate and stable forecasting of offshore wind power. Full article

The large-scale integration of wind and solar power introduces significant volatility into electricity markets, posing challenges for accurate day-ahead price forecasting for generation companies. This paper proposes a hybrid forecasting model, CEEMD-SE-IBA-LSTM, based on hyperparameter adaptation to improve prediction accuracy. First, a similar-day selection method integrating Random Forest and an Improved Grey Ideal Value approximation identifies the most relevant historical days. Second, Complete Ensemble Empirical Mode Decomposition with Sample Entropy (CEEMD-SE) decomposes and reconstructs the price series into stable components. Third, an Improved Bat Algorithm (IBA), incorporating differential evolution and adaptive weighting, is developed to optimize two key LSTM hyperparameters: the number of hidden layer neurons, which is treated as a model architecture hyperparameter, and the learning rate, which is treated as a training hyperparameter. The number of LSTM layers and the number of training epochs are kept fixed as model settings to ensure reproducibility. Using data from the US PJM market, the proposed model is validated against six benchmarks. The results show that CEEMD-SE-IBA-LSTM achieves superior performance, with a Mean Absolute Percentage Error (MAPE) of 3.73%, a Root Mean Square Error (RMSE) of 3.57 $/MWh, and a Mean Absolute Error (MAE) of 1.95 $/MWh. The method provides accurate price trends, offering effective decision support for new energy enterprises in price bidding to enhance revenue. Full article

Accurate day-ahead wind speed forecasting is essential for wind power forecasting and electricity market participation under increasing renewable energy penetration. This study proposes a hybrid forecasting framework that combines raw global forecasts from GFS and IFS, the KMA KIM-RDAPS regional forecast, and dynamically downscaled GFS/IFS forecasts generated with alternative boundary-layer physics. Seven forecast members were synthesized using arithmetic averaging, performance-weighted averaging, and LightGBM-based machine learning (ML) regression. The framework was evaluated over Jeju Island, Republic of Korea, using 10 m Automatic Weather Station observations from 2023 to 2024 and 80 m meteorological mast observations from 2023. For the AWS evaluation, 2023 was used for training and validation, and 2024 was reserved for independent testing. The site-specific LightGBM synthesis achieved the most consistent improvement, reducing the median site-wise MAE across 31 AWS sites to 0.90 m s⁻¹, corresponding to a 39.2% improvement relative to the best non-downscaled member and 47.2% relative to the unweighted multi-model mean. In the 80 m mast-based diagnostic assessment, the same approach reduced derived normalized power MAE to 11.4%. These results indicate that ML synthesis of multi-source NWP forecasts can improve day-ahead wind speed and power-oriented forecast information over complex island terrain. Full article

The trend in the power electronics industry toward higher power density and efficiency has brought high-frequency transformers (HFTs) to the forefront of critical applications, including isolated DC–DC converters, electric vehicle chargers, and solid-state transformers. This paper focuses on the leakage inductance of HFTs and presents a systematic comparative framework that evaluates five surrogate modeling and hybrid optimization approaches for the rapid and accurate estimation of leakage inductance. A comprehensive parametric dataset was constructed, comprising 1210 finite element analysis simulations conducted via finite element analysis in the ANSYS Maxwell 2024 R1 environment, varying the number of winding turns, primary winding thickness, and secondary winding thickness of the HFT. All five methods were trained and evaluated on the same dataset under identical conditions. The comparative evaluation demonstrates that the proposed hybrid Gray Wolf optimizer–artificial neural network (GWO-ANN) framework achieved the highest prediction accuracy (R² = 0.9832, MSE = 0.01780, MAE = 0.0935 µH) and the fastest convergence among all tested approaches. The generalization capability of the proposed model was confirmed through blind validation tests across six geometric configurations spanning the full range of the design space, yielding a maximum prediction error of 8.15% and an average error of 2.14%. The functional validity of the proposed parameters was further tested in a third validation layer using MATLAB/Simulink R2024b transformer circuit studies, demonstrating a theoretical efficiency of 96.06%. This three-layer validation approach proves both the parametric and functional reliability of the proposed framework for HFT designs. Full article

Accurate, uncertainty-aware estimation of instantaneous wind turbine output is a prerequisite for integrating onshore assets into low-emission energy systems, where operational monitoring, energy-performance verification, and cooperative asset management depend on auditable digital representations of turbine behaviour. This study develops a Digital Shadow-based power-curve modelling framework on fourteen years of Supervisory Control and Data Acquisition records from an operational Vestas V52 onshore turbine (850 kW, Dundalk Institute of Technology, Ireland; 457,429 ten-minute records spanning 2006–2020) and benchmarks seven methods under identical preprocessing on a strict chronological hold-out (training 2006–2017; testing 2018–2020; n = 52,388). A parallel random 75/25 split is reported only as a within-distribution diagnostic; it quantifies an optimistic R² inflation of 0.003–0.027 depending on architecture. The Artificial Neural Network attains the best chronological performance (R² = 0.9924, BCa 95% confidence interval 0.9910–0.9931, RMSE = 19.79 kW); only the ANN and a one-dimensional Convolutional Neural Network with twenty-four-step wind-speed lags (R² = 0.9921) deliver clear positive skill against the IEC-style manufacturer power curve. Split-conformal calibration of a Quantile Regression Forest raises empirical 90% prediction-interval coverage from 0.534 to 0.904 at a width inflation from 30 to 51 kW. The framework qualifies as a Digital Shadow and is positioned, through a Horizon Europe Technology Readiness Level audit and an explicit mapping to ISO 50001:2018 Plan–Do–Check–Act energy management and Renewable Energy Community governance under Directive (EU) 2018/2001, as an auditable monitoring layer for cooperative onshore wind operations. The empirical evidence base is a single turbine; multi-turbine, multi-site replication is the natural follow-on validation. Full article

This study presents a computational and experimental aerodynamic characterisation of a full-scale 5.5 m diameter, six-sail horizontal-axis windmill of the traditional Cretan Lasithi type, equipped with flexible woven polyester sails that act as a passive load-control mechanism. Seventeen operating points spanning wind speeds of 2.3–18.3 m/s were simulated in OpenFOAM using a transient sliding-mesh Arbitrary Mesh Interface formulation with the k–ω SST turbulence closure on a 2.3 million cell grid, selected on the basis of a four-level grid convergence study. CFD simulations identify three distinct aerodynamic regimes: a drag-dominated high-TSR regime (λ > 2.1), a mixed lift–drag working range with peak loading near λ ≈ 1.4–1.5, and a deep-stall regime in which boundary-layer separation propagates from root to tip as λ falls below 1.0. Field measurements conducted at the Energy Systems Synthesis Lab of the Hellenic Mediterranean University in compliance with IEC 61400-12-1:2005(E) confirm that rotor speed stabilises passively at 55–58 RPM above 13 m/s without any active control mechanism; CFD predictions agree with measured power output within 8–12% across the 2–13 m/s attached-flow envelope. The combined evidence indicates that passive overspeed self-regulation is driven by aeroelastic sail deformation, reducing effective disc solidity at high wind speeds, a mechanism that rigid-geometry CFD correctly identifies in trend but cannot quantify in magnitude. The primary limitation of the present work is the rigid-sail assumption of the CFD model, which requires a two-way coupled fluid–structure interaction extension as a future step. Full article

The rapid transition toward renewable energy requires transparent and spatially explicit methods for identifying suitable photovoltaic (PV) development areas. This study develops a geospatial artificial intelligence (GeoAI) decision-support framework for PV site suitability assessment in Rajasthan, India. Eleven harmonized predictors were used: global horizontal irradiance (GHI), photovoltaic power output (PVOUT), temperature, wind speed, aerosol optical depth (AOD), elevation, slope, albedo, land use/land cover (LULC), distance to roads, and distance to power lines. Reference labels were generated from an explicit rule-derived suitability index, class thresholds, and exclusion logic; therefore, the machine-learning task was to reproduce a transparent suitability framework rather than to predict observed PV yield or project-level performance. Extreme Gradient Boosting (XGBoost) was compared with simpler baseline models, evaluated using random and spatial-block validation, and interpreted using SHapley Additive exPlanations (SHAP). Independent overlays with known solar-installation records, presence-background robustness testing, and uncertainty/sensitivity analysis were used to examine spatial plausibility, spatial autocorrelation, deterministic label effects, and parameter uncertainty. The resulting outputs include pixel-level suitability zones, contiguous candidate polygons, district-level capacity-oriented summaries, and planning-priority classes. The framework is intended as a risk-aware regional screening tool: high model agreement indicates consistency with the constructed suitability labels, while final project decisions require parcel-scale land, grid, environmental, social, and economic assessment. Full article