Search Results (291)

The grid volatility caused by the integration of wind and solar power poses challenges to power systems, where Pumped Storage Hydropower (PSH) plays an irreplaceable role. During start-up, shutdown, and mode transition of pump turbines, near-zero flow conditions frequently occur, leading to severe hydraulic instability, guide vane vibration, and abnormal noise. This review synthesizes field observations from multiple high-head pumped storage stations together with recent experimental, numerical, and theoretical studies. The review indicates that hydraulic instability is primarily associated with the coupled effects of clearance leakage flow, bi-stable flow, and Rotor–Stator Interaction (RSI). The review suggests that self-excited vibration, rather than forced resonance, dominates guide vane vibration and abnormal noise under near-zero flow conditions. Four mainstream regulation strategies are summarized, including Misaligned Guide Vanes (MGVs), start-up/shutdown sequence optimization, structural-parameter adjustment, and operating range avoidance. The applicability and limitations of each strategy are discussed. These findings provide support for the design and operation of high-head, large-capacity pump turbines. Full article

To effectively extract fault characteristics from complex vibration signals and improve the diagnostic performance of deep learning networks, this paper introduces a wind turbine blade fault diagnosis method that combines Multi-scale Enhanced Hierarchical Fuzzy Entropy (MEHFE), Isolation Forest, and the Grey Wolf Optimization (GWO) algorithm for optimizing the Gated Recurrent Unit (GRU). Initially, the MEHFE algorithm is applied to decompose and reconstruct three-directional vibration signals at the blade root, thereby extracting “scale-frequency” dual-dimensional features that represent the evolution of fault frequency structure and complexity across multiple scales. Subsequently, Isolation Forest is employed to assess and filter feature importance, constructing an optimal feature subset to mitigate redundancy and noise interference. Finally, the optimal features are fed into the GRU network for fault pattern recognition, and the GWO algorithm is utilized to adaptively optimize network hyperparameters, thereby enhancing classification accuracy and noise resilience. Simulation experiments on typical wind turbine blade faults reveal that when GRU serves as the classifier, the diagnostic accuracy of MEHFE exceeds 76%. After feature optimization with Isolation Forest and network parameter optimization with GWO, the diagnostic accuracy surpasses 93%, demonstrating notable advantages in both classification capability and stability. Even under conditions of noise interference, the accuracy remains above 90%. The research substantiates that the proposed method can effectively extract pattern information indicative of blade structural damage from vibration data, achieving high fault recognition accuracy and robustness. Full article

To address the high dimensionality, redundancy, and noise interference present in wind turbine Supervisory Control and Data Acquisition (SCADA) data, as well as the limitations of conventional K-means algorithms—including excessive reliance on manual parameter tuning and weak anti-noise performance—this paper proposes a Variable Grid Optimized K-means (VGO-K-means) preprocessing algorithm. Z-score standardization is adopted to unify feature magnitudes. Meanwhile, an adaptive grid density calculation strategy is developed to dynamically adjust grid resolution according to the value range of each dimension, enabling accurate characterization of the spatial distribution of monitoring samples. Furthermore, a multi-index voting mechanism integrating the silhouette coefficient, Davies–Bouldin index, and inertia metric is established to adaptively determine the optimal cluster number without manual intervention. Utilizing the density discrepancy between dense normal samples and sparse outliers, the proposed method identifies abnormal samples through a clustering density threshold. Validated on real wind farm SCADA data containing 892 manually labeled abnormal samples, the VGO-K-means algorithm achieves a precision of 96.8% and an F1-score (the harmonic mean of precision and recall) of 0.89. Under identical test conditions, it outperforms traditional K-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and fixed-grid K-means methods. The entire workflow consumes only 3.9 s, achieving an excellent balance between detection accuracy and computational cost. The proposed framework provides a reliable and practical preprocessing solution for wind turbine condition monitoring data. Full article

Reliable condition monitoring of wind turbine gearboxes is critical to reducing unplanned downtime and maintenance costs in wind farms. However, this task presents significant challenges due to the non-stationary nature of vibration signals, in which fault-relevant features are sparsely and unevenly distributed across the time–frequency map. Although time–frequency analysis has been widely adopted to represent nonlinear and non-stationary vibration signals, existing deep learning methods typically process the full spectrogram directly, without distinguishing redundant or uninformative regions. This leads to high input dimensionality and exposes the model to substantial spectral noise. Consequently, it increases computational burden and potentially reduces the diagnostic reliability. To address this issue, this paper proposes a two-stage hybrid framework based on complementary selection mechanisms operating on two distinct feature spaces. In the first stage, eXtreme Gradient Boosting (XGBoost) importance scores are used to identify and permanently prune uninformative time–frequency features from the input spectrogram, reducing the input map size by 25%. In the second stage, a Squeeze-and-Excitation (SE) block, inserted after the deepest residual layer, performs soft channel-wise recalibration of the abstract feature maps produced by the residual convolutional neural network (ResCNN), thereby amplifying discriminative representations prior to classification. The proposed method was evaluated in an eight-class variable-speed fault classification task using the MCC5-THU benchmark, where data were collected from a 2.2 kW motor-driven gearbox test rig. The proposed method achieves a mean accuracy of 97.81% ± 0.33% under 5-fold stratified cross-validation (CV), while reducing classifier training time by approximately 23% compared to a baseline model trained on the full spectrogram. These results demonstrate that explicit input-level spectrogram pruning, combined with model-level channel attention, yields a robust and computationally efficient diagnostic framework for wind turbine gearbox condition monitoring. Full article

Reliable and rapid forecasting of wind turbine output power is vital for operators, particularly day-ahead and intraday market scheduling and reserve allocation. However, the inherent unpredictability, intermittency, and volatility of wind turbine output make forecasting processes difficult. To address this challenge, this study proposes a Sinusoidal Representation Network (SIREN)-based forecasting model for high-accuracy, rapid direct multi-horizon forecasting of wind turbine output power. SIREN is selected due to the periodic and symmetrical mathematical structure of its sinusoidal activation function, which allows the model to represent both low-frequency trends and high-frequency sudden changes in wind energy data. To improve data quality, compensate for asymmetric fluctuations in wind data, and provide more suitable inputs for SIREN training. Several preprocessing steps are utilized before feeding the data into the model. The proposed preprocessing step includes a moving median filter, robust scaling based on median and interquartile range, Winsorizing clipping, and a Hampel filter to reduce the effects of instantaneous noise, outliers, and local peaks without disrupting temporal continuity. Subsequently, a Savitzky–Golay smoothing is applied to attenuate high-frequency measurement noise while preserving curvature, local peaks, and physically meaningful short-term dynamics in the data. The sliding-window approach is used to formulate the multi-horizon forecasting problem directly, and a direct h-step-ahead forecasting architecture is designed, preserving structural symmetry in the time series. The SIREN is trained and tested using MATLAB with the help of two different datasets: Dataset-1 has a 10 min resolution for 1 year, and Dataset-2 has a 1 h resolution for 15 years. The forecast horizon parameter h is considered separately for each step, and the proposed SIREN is independently trained, validated, and tested for each target horizon while maintaining chronological order. The results demonstrate that the proposed model is able to yield high forecast performance for a wide spectrum of horizons ranging from 10 min to 15 days. The accuracy of the proposed model for Dataset-1 is R² of 99.6%, MSE of 0.085%, MAE of 1.7%, and MAPE of 12%, while for Dataset-2, the accuracy is R² of 98.8%, MSE of 0.3%, MAE of 3.6%, and MAPE of 23%. Ablation and sensitivity analyses are conducted to evaluate the impact of the basic components used in the proposed model on forecasting performance. In addition, combative experiments are performed using traditional time series, ML, and DL forecasting techniques to better assess the contribution of the model. The obtained results show that the SIREN-based direct forecasting approach provides strong learning capability, as well as high forecasting accuracy, for both high-resolution and low-resolution wind power data. Overall, its ability to capture the symmetric and periodic characteristics inherent in wind turbine power data makes it a promising alternative for multi-horizon wind power forecasting applications. Full article

All rotating electrical machines are susceptible to vibrations arising from electromagnetic (EM) forces, electrical faults, mechanical defects, imbalance, and structural resonance. In Doubly Fed Induction Generators (DFIGs), such electromechanical vibrations are especially important because they can degrade reliability, increase noise, and lead to severe damage if resonance-prone operating conditions are not identified in time. Although fault diagnosis in DFIGs has been widely investigated using current, voltage, and flux signatures, comparatively fewer studies have examined fault-specific vibration behaviour under stator and rotor interturn faults (ITTFs), particularly through a coupled EM structural framework. In addition, prior vibration-based studies have not examined the influence of end winding ITTFs, its location, severity, and modal interaction investigating resonance risk. This paper considers vibration characteristics of a variable-speed 2.8 MW DFIG used in a grid-connected Type-3 wind turbine unit (WTU) at no-load operating condition. The DFIG is modelled in ANSYS Academic Research v 2022 R2 Maxwell for EM behaviour assessment for ITTFs in both stator and rotor windings along with modal analysis (MA) in ANSYS Workbench to examine the undamped stator and rotor modes over a range of frequencies. This coupled approach enables identification of vibration signatures associated with different ITTF types. The results show the magnetic flux density near faulty end-winding region increases with fault severity and ranges from 4.19 T to 4.39 T in proximity to faulty windings. A dominant modal frequency band of 60–65 Hz is identified, where stator and rotor modes coincide, creating probable resonance conditions. A severe vibration response is observed for single-phase stator ITTF, showing an amplitude of 2116 mm/s at 480 Hz for a larger number of shorted turns, indicating that asymmetric faults can produce stronger EM excitation than multi-phase faults. The main contribution of this paper is demonstration of a fault-specific, MA and vibration-based Condition monitoring system (CMS) implementation workflow for a DFIG. Unlike prior vibration-based studies that primarily focus on general machine vibration, mechanical faults, bearings, etc., this paper links stator and rotor ITTF induced EM excitation to modal characteristics, resonance behaviour, and measurable vibration signatures, establishing vibration analysis (VA) as a practical complementary technique for CMS of ITTFs in DFIGs. Full article

Anomaly detection models trained exclusively on healthy data are widely used in wind turbine condition monitoring because failure data are scarce, heterogeneous, and often unavailable. However, these models produce anomaly indicators that are sensitive not only to fault-related degradation but also to normal operational variability, transient disturbances, and changes in loading conditions. As a result, the practical behavior of an alarm system depends not only on the anomaly detection model but also on the decision rule used to activate and maintain alarm states. This study presents a decision-oriented evaluation of persistence-based alarm confirmation in wind turbine anomaly detection. Four representative techniques are analyzed within a unified framework: Isolation Forest, One-Class Support Vector Machine, Referenced Moving Window Principal Component Analysis using Q-statistic and percentage component weight indicators, and Autoencoder-based reconstruction error. The evaluation combines controlled OpenFAST simulations of rotor unbalance under different severity and noise conditions with an industrial SCADA case study involving a documented main bearing fault. Results show that temporal persistence strongly shapes alarm outcomes across methods and datasets. Low persistence values favor early detection but promote alarms from isolated threshold exceedances, whereas moderate persistence substantially reduces false positives while preserving detection capability in severe and well-observable faults. Excessive persistence increases detection latency and missed detections, particularly for weak, intermittent, or slowly evolving fault signatures. These findings indicate that persistence-based alarm confirmation should be treated as an explicit decision-level configuration variable, rather than as a fixed post-processing or alarm-state heuristic, when designing anomaly detection systems for wind turbine condition monitoring. Full article

The rapid expansion of wind energy as a key pillar of sustainable electricity generation has intensified the need for reliable and efficient wind turbine operation, particularly in minimizing failures of critical components such as gearboxes, which significantly impact maintenance costs, downtime, and overall lifecycle sustainability. This study proposes a vibration-based fault diagnosis framework integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a Multiscale Convolutional Neural Network (MSCNN) for wind turbine gearbox condition monitoring. The approach decomposes non-stationary vibration signals into Intrinsic Mode Functions (IMFs) to capture meaningful oscillatory characteristics, which are then processed through parallel multiscale convolutional branches to learn both transient and long-term signal patterns. Experimental validation using the NREL Gearbox Reliability Collaborative dataset demonstrates that the proposed CEEMDAN-MSCNN model demonstrates strong performance compared to conventional machine learning methods and single-scale CNN architectures, achieving 99.50% accuracy on an unseen holdout dataset. The proposed framework supports predictive maintenance strategies by enabling reliable fault diagnosis, reducing unplanned downtime, and improving the operational efficiency and long-term sustainability of wind energy systems. Full article

As the offshore wind industry scales toward 15 MW capacity, the reliability of Direct-Drive Permanent Magnet Synchronous Generators (DD-PMSGs) becomes critical. However, real-world run-to-failure data for these massive, multi-pole machines is virtually non-existent, creating a barrier for developing effective data-driven diagnostic systems. This study proposes a high-fidelity framework for detecting permanent magnet faults in the International Energy Agency (IEA) 15 MW Reference Wind Turbine. Using Finite Element Analysis (FEA), a dataset (magnetic flux and back electromotive-force (EMF)) capturing the electromagnetic signatures of healthy and faulty states of a PMSG under varying severities is generated. To improve the power of computer vision, 1D time-series signals were transformed into 2D images. Specifically, Gramian Angular Fields (GAFs) and Recurrence Plots (RPs) were applied to magnetic flux density signals, while Markov Transition Fields (MTFs) were applied to back-EMF signals. These representations were then fused into multi-channel Red-Green-Blue (RGB) images and processed via a ResNet-18 Deep Transfer Learning model using a strictly non-overlapping, leakage-free dataset partitioning strategy. The proposed framework achieved a classification accuracy of 99.45% on noise-free data. Furthermore, robustness testing under varying levels of Additive White Gaussian Noise (AWGN) (30 dB, 40 dB, and 50 dB Signal-to-Noise Ratio (SNR)) demonstrated sustained high performance, maintaining over 90% accuracy even under severe 30 dB noise conditions. Comparative analysis proved that this multi-channel fusion significantly outperforms single-channel encoding methods, which collapse under heavy noise, validating the scalability of the framework and applicability for next-generation condition monitoring in harsh offshore environments. Full article

Wind turbine blades are prone to multiscale and weak-feature damage in complex natural environments. Accurate and efficient detection is crucial for ensuring the safe operation of wind turbine units. However, existing models struggle to balance detection precision, robustness, and lightweight deployment requirements. In this paper, we propose a lightweight model, Wind-YOLO, for wind turbine blade defect detection based on YOLOv11, with three core innovations: (1) We design a DynamicC3k2 that adaptively adjusts the convolutional receptive field for feature extraction, enhancing fine-grained feature capture of micro-cracks and weak-texture defects. (2) We construct a Cross-Stage Partial with Focused Linear Attention (C2FLA) that precisely focuses on defect regions via a linear attention mechanism, effectively mitigating complex background and noise interference. (3) We propose a Spatially Guided Gated Feature Pyramid Network (SGG-FPN) that optimizes multiscale feature transmission and aggregation through a gated fusion mechanism, improving adaptability to cross-scale defects from millimeter-level cracks to meter-level spalling. Extensive experiments on a dedicated wind turbine defect dataset show that Wind-YOLO achieves an mAP@0.5 of 80.9% and an mAP@0.5:0.95 of 37.1%, achieving an increase of 3.9 percentage points and 2.4 percentage points, respectively, compared with the baseline YOLOv11. Meanwhile, the model has only 2.34 million parameters (2.34 M) and a computational complexity of 6.0 GFLOPs. It delivers dual improvements in precision and lightweight performance, with superior environmental adaptability for real-time wind turbine inspection. Full article

Accurate Structural Health Monitoring of offshore wind turbines is critical for ensuring their long-term operational safety in harsh marine environments. Although displacement is a fundamental metric for assessing structural deformation and stress distribution, its direct measurement in open-ocean conditions is severely hindered by environmental interference and the absence of stable spatial references. Consequently, reconstructing displacement from structural acceleration through double integration is widely adopted, yet it suffers from severe baseline drift. Furthermore, existing drift-mitigation techniques often rely on empirical parameter selection and are limited to single-point reconstructions, failing to capture the full three-dimensional (3D) spatial attitude of the structure. To address these limitations, this paper proposes a novel 3D spatial attitude reconstruction framework based on advanced drift removal and spatial interpolation. First, an improved drift removal algorithm is developed to accurately eliminate baseline errors from acceleration signals, ensuring the physical fidelity of the reconstructed local displacements. Subsequently, cubic spline interpolation is utilized to extrapolate these discrete local measurements into a comprehensive full-field attitude profile of the entire turbine structure. The performance and robustness of the proposed method are systematically verified through numerical simulations and finite element analysis. Finally, its engineering applicability and accuracy are further validated via laboratory experiments and field measurements. The proposed framework effectively mitigates noise sensitivity and significantly enhances the accuracy of full-field attitude reconstruction, providing a reliable foundation for refined structural health assessments of OWTs. Full article

An approximate analytical model for the variation of A-weighted broadband sound levels with distance over flat acoustically soft ground from a source of known sound power depends on the reduction in low frequency content in noise spectra due to A-weighting. Also, it assumes a weak linear sound speed gradient and a frequency independent attenuation coefficient for air absorption. The model introduces adjustable frequency independent parameters for ground effect, turbulence and atmospheric refraction. An additional parameter allows for the source being located over acoustically hard ground. Predictions of the model are compared with measurements over several ground surfaces. The approximate model predicts a more rapid reduction in sound attenuation due to ground effect with increasing mean propagation path height than the simplified method in a widely used international standard. Moreover, predictions of A-weighted sound levels from onshore wind turbines using the approximate analytical method compare with data and numerical simulations better than the simplified and octave band methods in the international standard and the Swedish standard method. Full article

Wind is a significant contributor to global energy requirement, with technological advancements in this industry enabling its rapid growth over the last few decades. The rise in demand for clean energy provides the driving factor to make wind more efficient and widespread. One such solution involves mitigating the aerodynamic noise of wind turbine rotors to harness untapped energy and improve turbine efficiency. Quieter wind turbines gain community acceptance, promoting their widespread application. This article explores passive compliant coatings applied to a flat plate under fully turbulent conditions through Computational Fluid Dynamics (CFD) and wind tunnel testing. It extends prior flat plate investigations by evaluating the noise mitigation potential of passive compliant coatings in the context of wind turbine trailing edge (TE) noise. Two coatings with distinct material properties were investigated through Computational Aeroacoustics Analysis (CAA) and Fluid–Structure Interaction (FSI). While coating-1 (Dow Corning Silastic S-2) increased the overall sound pressure level (OASPL) by 2.89 dB, coating-2 (Dow Corning Sylgard 184) reduced TE noise by 2–4 dB/Hz between 600 and 1575 Hz and lowered the OASPL by 1.85 dB. Within the two configurations investigated, the differences in noise mitigation characteristics may be attributed to variations in coating stiffness and geometric compliance. Based on these simulations, wind tunnel tests were conducted to record noise measurements using coating-2 which revealed a 3.23 dB OASPL reduction, suggesting its suitability for wind turbine noise mitigation applications. Full article

The increasing deployment of wind energy has brought renewed attention to aeroacoustic noise generated by wind turbine blades, where broadband noise is primarily associated with vortex shedding at the trailing edge (TE) and leading edge (LE) of airfoils. Owls, particularly Tyto alba, exhibit wing morphologies such as serrations, velvet-like surfaces, and fringes that enable silent flight through aerodynamic noise suppression. This study presents a scoping review of the scientific literature on owl-inspired serration strategies applied to aerodynamic airfoils and wind turbine blades. The literature search was conducted across major academic databases, including Scopus, ScienceDirect, SpringerLink, and MDPI, covering publications from 1970 to 2025. A total of 69 experimental and numerical studies focusing on LE and TE serrations was analyzed. The review integrates aeroacoustic analysis with bio-inspired design perspectives. The analyzed studies consistently show that serrated geometries modify vortex dynamics and turbulence structures, leading to measurable acoustic benefits. Experimentally, the largest reductions reported for aerodynamic airfoils reached about 7 dB for both LE and TE serrations, mainly as broadband noise attenuation, in specific frequency ranges. Numerically, the highest reported reduction reached up to 21 dB for a serrated TE configuration, corresponding to spectral SPL reduction mainly below 1.6 kHz. The reviewed studies also indicate that the associated aerodynamic response is strongly configuration-dependent, ranging from limited penalties to measurable changes in lift, drag, power output, or structural loading. Numerical simulations further support experimental findings and highlight the importance of geometric parameters such as serration amplitude, wavelength, and spacing. Overall, bio-inspired serrations represent a promising passive strategy for aeroacoustic noise mitigation in wind turbines, drones, and rotating aerodynamic systems. Future research should focus on the multi-objective optimization of serration geometry, large-scale experimental validation, and the integration of bio-inspired concepts into industrial blade designs. Full article

The study focuses on the infrared nondestructive detection of inclusion-type internal defects in glass-fiber-reinforced plastic (GFRP) wind turbine blade specimens, which were designed to simulate the laminated material structure and typical hidden defects of in-service blades. To address the difficulty of detecting internal defects in in-service wind turbine blades, this paper establishes an active thermal imaging defect detection and recognition system using a halogen lamp as the infrared thermal excitation source and a high-resolution thermal imaging camera as the detection component. To improve the recognition of defect contour information in infrared images, a method combining bidimensional filtering empirical mode decomposition (BFEMD), Gaussian filtering, and Otsu threshold segmentation is proposed. The BFEMD procedure decomposes the infrared image into bidimensional intrinsic mode function components and residual components, Gaussian filtering suppresses noise in the selected components, and Otsu threshold segmentation extracts the defect contours. Experimental results show that the combined algorithm can enhance defect targets in infrared images, improve visibility and contour integrity, and provide a higher detection rate for wind turbine blade defects under different defect depths and materials. Full article