Search Results (181)

Transmission towers are particularly vulnerable to extreme wind events, which can lead to structural damage or collapse, thereby compromising the stability of power transmission systems. Enhancing the wind-resistant capacity of these towers is therefore critical for improving the reliability and resilience of electrical infrastructure. This study utilizes finite element analysis (FEA) to evaluate the structural response of a 220 kV transmission tower subjected to fluctuating wind loads, effectively capturing the dynamic characteristics of wind-induced forces. A comprehensive dynamic analysis is conducted to account for uncertainties in wind loading and variations in wind direction. Through this approach, this study identifies the most critical wind angle and local structural weaknesses, as well as determines the threshold wind speed that precipitates structural collapse. To improve structural resilience, a concurrent multi-scale modeling strategy is adopted. This allows for localized analysis of vulnerable components while maintaining a holistic understanding of the tower’s global behavior. To mitigate failure risks, the traditional perforated plate reinforcement technique is implemented. The reinforcement’s effectiveness is evaluated based on its impact on load-bearing capacity, displacement control, and stress redistribution. Results reveal that the critical wind direction is 45°, with failure predominantly initiating from instability in the third section of the tower leg. Post-reinforcement analysis demonstrates a marked improvement in structural performance, evidenced by a significant reduction in top displacement and stress intensity in the critical leg section. Overall, these findings contribute to a deeper understanding of the wind-induced fragility of transmission towers and offer practical reinforcement strategies that can be applied to enhance their structural integrity under extreme wind conditions. Full article

Amid the global shift toward clean energy, wind power has emerged as a critical pillar of the modern energy matrix. To improve the reliability and maintainability of wind farms, this work proposes a novel hybrid fault detection approach that combines expert-driven diagnostic knowledge with data-driven modeling. The framework integrates autoencoder-based neural networks with Failure Mode and Symptoms Analysis, leveraging the strengths of both methodologies to enhance anomaly detection, feature selection, and fault localization. The methodology comprises five main stages: (i) the identification of failure modes and their observable symptoms using FMSA, (ii) the acquisition and preprocessing of SCADA monitoring data, (iii) the development of dedicated autoencoder models trained exclusively on healthy operational data, (iv) the implementation of an anomaly detection strategy based on the reconstruction error and a persistence-based rule to reduce false positives, and (v) evaluation using performance metrics. The approach adopts a fault-specific modeling strategy, in which each turbine and failure mode is associated with a customized autoencoder. The methodology was first validated using OpenFAST 3.5 simulated data with induced faults comprising normal conditions and a 1% mass imbalance fault on a blade, enabling the verification of its effectiveness under controlled conditions. Subsequently, the methodology was applied to a real-world SCADA data case study from wind turbines operated by EDP, employing historical operational data from turbines, including thermal measurements and operational variables such as wind speed and generated power. The proposed system achieved 99% classification accuracy on simulated data detect anomalies up to 60 days before reported failures in real operational conditions, successfully identifying degradations in components such as the transformer, gearbox, generator, and hydraulic group. The integration of FMSA improves feature selection and fault localization, enhancing both the interpretability and precision of the detection system. This hybrid approach demonstrates the potential to support predictive maintenance in complex industrial environments. Full article

Gearbox failure represents one of the most time-consuming maintenance challenges in wind turbine operations. Abnormal temperature variations in the gearbox high-speed shaft (GHSS) serve as reliable indicators of potential faults. This study proposes a Spatio-Temporal Attentive (STA) synergistic architecture for GHSS fault detection and early warning by utilizing the in situ monitoring data from a wind farm. This comprehensive architecture involves five modules: data preprocessing, multi-dimensional spatial feature extraction, temporal dependency modeling, global relationship learning, and hyperparameter optimization. It was achieved by using real-time monitoring data to predict the GHSS temperature in 10 min, with an accuracy of 1 °C. Compared to the long short-term memory (LSTM) and convolutional neural network and LSTM hybrid models, the STA architecture reduces the root mean square error of the prediction by approximately 37% and 13%, respectively. Furthermore, the architecture establishes a normal operating condition model and provides benchmark eigenvalues for subsequent fault warnings. The model was validated to issue early warnings up to seven hours before the fault alert is triggered by the supervisory control and data acquisition system of the wind turbine. By offering reliable, cost-effective prognostics without additional hardware, this approach significantly improves wind turbine health management and fault prevention. Full article

This research introduces an enhanced fault detection approach, variational mode decomposition (VMD), for identifying open-circuit IGBT faults in the grid-side converter (GSC) of a doubly fed induction generator (DFIG) wind turbine system. VMD has many advantages over other decomposition methods, notably for non-stationary signals and noise. VMD’s robustness stems from its ability to decompose a signal into intrinsic mode functions (IMFs) with well-defined centre frequencies and bandwidths. The proposed methodology integrates VMD with a hybrid convolutional neural network–long short-term memory (CNN-LSTM) architecture to efficiently extract and learn distinctive temporal and spectral properties from three-phase current sources. Ten operational scenarios with a wind speed range of 5–16 m/s were simulated using a comprehensive MATLAB/Simulink version R2022b model, including one healthy condition and nine unique IGBT failure conditions. The obtained current signals were decomposed via VMD to extract essential frequency components, which were normalised and utilised as input sequences for deep learning models. A comparative comparison of CNN-LSTM and CNN-only classifiers revealed that the CNN-LSTM model attained the greatest classification accuracy of 88.00%, exhibiting enhanced precision and resilience in noisy and dynamic environments. These findings emphasise the efficiency of integrating advanced signal decomposition with deep sequential learning for real-time, high-precision fault identification in wind turbine power electronic converters. Full article

This article presents the findings of an experimental study conducted on a vertical axis wind turbine (VAWT) tower instrumented with cascaded fiber Bragg grating (FBG) sensors to detect bending deformations. Structural health monitoring (SHM) is an essential need in the industry to reduce costs and maintenance time, and to prevent machine failures. First, FBG strain sensors were glued vertically along the tower to investigate the sensors behavior as a function of their height. The maximum signal-to-noise ratio is obtained when FBGs are placed at the tower base. Then, four packages were installed inside the tower, at the base, according to four cardinal directions. Each package contains an FBG strain sensor, and an extra temperature FBG for discrimination. The use of easy-to-deploy packages is a must for industrial installations. Afterwards, by using power spectral density (PSD) on the strain signals, three sources of tower oscillations are discovered: wind force, structure unbalance, and 1st tower mode resonance, each with its intrinsic frequency. Wind force and structure unbalance cause mechanical stresses at a frequency proportional to the wind turbine rotational speed, while the 1st tower mode frequency depends only on the machine geometry, regardless of the rotational speed. This study also analyzes the deformation amplitude for different rotational rates within the VAWT operational range (10–35 rpm). The resonance amplitude depends on the proximity of the rotational rate to the resonant frequency (22 rpm) and the duration at that rate. For structure unbalance, the oscillation amplitude increases with the rotational rate, due to the centrifugal effect. It is supposed that wind force deformation amplitude naturally depends on wind speed, which is unpredictable at a given precise time. The results of our experimental observations are very valuable for both the wind turbine manufacturer and owner. Full article

Extreme sea conditions, particularly extreme operation gusts (EOGs), present a substantial threat to structures like floating offshore wind turbines (FOWTs) due to the intense loads they exert. In this work, we simulate EOGs and analyze the dynamic response of floating wind turbines. We conduct separate analyses of the operational state under the rated wind speed, the operational state, and the shutdown state under the EOG, focusing on the motion of the floating platform and the tension of the mooring lines of the FOWT. The results of our study indicate that under the influence of EOGs, the response of the FOWT changes significantly, especially in terms of the range of response variations. After the passage of an EOG, there are notable differences in the average response of each component of the wind turbine under the shutdown strategy. When compared to normal operation during EOGs, the shutdown strategy enables the FOWT to reach the extreme response value more rapidly. Subsequently, it also recovers response stability more quickly. However, a FOWT operating under normal conditions exhibits a larger extreme response value. Regarding pitch motion, the maximum response can reach 10.52 deg, which may lead to overall instability of the structure. Implementing a stall strategy can effectively reduce the swing amplitude to 6.09 deg. Under the action of EOGs, the maximum mooring tension reaches 1376.60 kN, yet no failure or fracture occurs in the mooring system. Full article

Inter-turn short-circuit fault is a common electrical issue in high-speed train traction motors, which can severely degrade motor performance and significantly shorten operational lifespan. Early detection is crucial for ensuring the safety of traction systems. This paper presents a digital twin-based method for diagnosing stator winding inter-turn short-circuit faults in induction motors. First, an advanced rapid-solving algorithm is employed to establish a real-time digital twin model of the motor under healthy conditions. Second, a mathematical model characterizing stator winding faults is developed. Subsequently, fault detection and localization are achieved through analyzing three-phase current residuals between the digital twin model and the actual system. Extensive simulations and experiments demonstrate that the proposed method generates a fault index amplitude approximately 20 times larger than traditional sampling-value-based prediction methods, indicating exceptional sensitivity. The approach is minimally invasive, requiring no additional measurement equipment. Moreover, it maintains diagnostic capability even under motor parameter mismatch conditions, outperforming traditional methods. The proposed method demonstrates distinct advantages for high-speed train traction systems. It enables real-time monitoring and predictive maintenance, effectively reducing operational costs while preventing catastrophic failures. Full article

The development of lightweight de-icing equipment for partial transmission lines in a microtopography area has become a hot research topic. However, the existing local line de-icing methods pay less attention to the mechanical damage caused by unequal tension on the tower, and there is a lack of safe de-icing strategies. This study has proposed a methodology integrating an orthogonal experimental design and finite element mechanical analysis to assess the impact of localized line de-icing on the structural stability of transmission tower-line systems. Taking the ±800 kV transmission line as an example, the refined finite element model of the transmission tower-line system has been established, the influence of each conductor and ground wire defrosting on the tower has been analyzed, and a scientific de-icing strategy has been formulated. Thus, the critical ice thickness and wind speed curves for tower failure have been calculated. The research results show that the de-icing of conductor 1, 5, 6, and ground wires 11 and 12 has a higher impact on the failure of the entire tower-line system. Ice melting on the windward side and ice covering on the leeward side will cause the unbalanced tension of the tower to be greater. The findings provide actionable guidelines for the formulation of a transmission line de-icing strategy and reduce the damage caused by ice. Full article

This study investigates the reliability of tall buildings subjected to dynamic across-wind loading, focusing on the Tuned Mass Damper Fluid Inerter (TMDFI). While existing literature emphasises the effectiveness of TMDFI in mitigating seismic hazards, research on its reliability regarding wind hazards remains limited. A wind-sensitive benchmark 76-storey building is modeled to compare the performance of the TMDFI against a traditional tuned mass damper (TMD) and an uncontrolled structure. A Monte Carlo Simulation (MCS) approach comprising 31,500 simulations is employed to assess reliability under uncertain damping ratios and varying turbulence intensities at reference wind speeds of 20 to 40 m/s. Key performance metrics, including peak acceleration and root mean squared (RMS) displacement responses, are derived through spectral analysis in the frequency domain. Results indicate that the TMDFI offers superior reliability, allowing an additional 6–7 m/s in reference velocity before reaching significant failure at the ISO limit state. Peak acceleration and RMS displacement are reduced by up to 64% to the uncontrolled structure. The TMDFI consistently outperforms both the TMD and uncontrolled configurations across all turbulent cases and wind velocities examined. Full article

This paper presents a vibration analysis method for detecting typical faults in gears of the drive train of a 2 MW wind turbine. The data were collected over a one-year period from an operating wind turbine with a gearbox composed of one planetary stage and two helical gear stages. Failures in two pairs of helical gears were identified: one involving pitting and wear in the gears connecting the intermediate-speed shaft to the low-speed shaft, and another one involving significant material detachment in the gears connecting the intermediate-speed shaft to the high-speed shaft. The continuous evaluation of time signals, frequency spectra, and amplitude modulations allowed the most sensitive sensors and frequencies for predicting surface damage on gear teeth in this type of turbine to be determined. A steady-state frequency analysis was performed, enabling the detection of the aforementioned surface faults. This approach is simpler compared with more complex transient-state techniques. By tracking vibration signals over time, the importance of analyzing gear mesh frequencies and their harmonics was highlighted. Additionally, it was found that the progression of gear damage was dependent on the power output of the wind turbine. As a result, the most appropriate ranges of power were identified, within which the evolution of the vibration measurement was associated with the damage evolution. Since many turbines currently in operation have similar designs and power output levels, the present findings can serve as a guideline for monitoring an extensive number of units. Full article

Traditional construction of timber houses in Tanzania has been prevalent for years; however, inhabiting these structures has been a challenge due to the instability of the buildings under various loadings. This instability, despite its lightweight, is mainly controlled by mechanical joints within timber members. Parametric Python scripts were developed in Abaqus (version 6.13) to have a reliable joint between timber volume modules and assess their response when subjected to wind forces. Two timber volume modules, each with a height of 3.0 m, were subjected to a horizontal displacement of 10 mm. Results show that the screwed fasteners between the modules result in high shear resistance due to the embedded fastener’s threads in timber members increasing the rope effect. Additionally, with weak fastener stiffness, the openings in the longitudinal wall had no effect on resisting shear compared to strong joints between modules. Longitudinal walls with doors and window openings showed a decrease in shear force to 21.95 kN, which is 44% less than the 39 kN of walls without openings. In addition, for a single door in the wall, the shear force decreased to 17.9%, indicating that major shear forces in the wall are affected by the window opening due to its large size and proximity to the point of load application. Furthermore, the stresses were concentrated in the corners of the openings, subjecting the structure to failure during its in-service life and demanding the use of cross-diagonal timber members between the corners to redistribute corner stresses. It is recommended that these types of houses be adopted due to less slip deformation (less than 10 mm) caused by wind speed of 24 km/h. Full article

Power transmission tower-line systems are exposed to various dynamic hazards, including wind and earthquakes, among others. Despite the multitude of dampers proposed to mitigate vibrations, the dual control effect on both seismic and wind-induced vibrations has rarely been addressed. This paper introduces a comprehensive methodology for evaluating the reliability of power transmission towers under a range of dynamic disasters, encompassing both earthquakes and wind loads. Subsequently, a lifelong reliability approach was employed to assess the efficacy of a pounding tuned mass damper (PTMD). The proposed algorithm leverages the incremental dynamic analysis (IDA) method to compute structural fragility with regard to each type of disaster and integrates these findings with hazard functions to determine the probability of overall failure. The results conclusively demonstrate that the PTMD substantially diminished the towers’ dynamic response to both earthquakes and wind loads, thereby enhancing their overall reliability. Specifically, the PTMD reduced the vibration reduction ratio by 10% to 30% under wind loads and by 20% to 80% under seismic actions, with more pronounced effects at higher wind speeds and peak ground accelerations (PGAs). Furthermore, the reliability index (β) of the transmission tower increased from 2.1849 to 2.4295 when the PTMD was implemented, highlighting its effectiveness in dual-hazard scenarios. This study underscores the potential for reliability to be considered as a key metric for optimizing damping devices in power transmission structures, particularly in the context of multi-hazard scenarios. Full article

The marine environment’s complexity poses considerable difficulties for the stability and reliability of communication links. The restricted coverage of onshore base stations in marine areas makes relay technology a critical solution for extending the communication coverage. Here, connectivity analyses help nodes select the optimal forwarding links, reducing transmission failures and improving the network performance. However, the rapid changes in marine wireless channels and the complexity of hydrological conditions make it challenging to acquire precise channel state information (CSI). In particular, dynamic environmental factors like tides, waves, and wind speed lead to substantial variations in the channel parameters over time. In response to these challenges, this paper puts forward a ship-to-shore communication system using relay ships to extend the coverage of terrestrial base stations. A novel channel modeling method is designed to capture the characteristics of marine wireless channels accurately. Additionally, a machine learning (ML)-based approach is introduced to predict the dual-hop link connection probability at future time points by analyzing historical time-series data on oceanic environmental and ship movement parameters. The proposed model consists of a convolutional-layer-based feature extractor and a bidirectional long short-term memory (BiLSTM) estimator. The CNN module extracts effective high-level features from the input data, while the BiLSTM module further explores the dependencies and dynamic patterns along the temporal dimension. The attention mechanism is introduced to distinguish the importance of the information through a weighted approach. The experimental results show that compared to traditional methods and other deep learning approaches, the proposed CNN-BiLSTM-AM model performs better in terms of its prediction accuracy and fitting ability. The model’s mean squared error (MSE) is as low as 0.0126. Full article

This paper presents a novel orthogonal octo-rotor Unmanned Aerial Vehicle (UAV) and addresses the aerodynamics of the UAV with varied rotor spacing by both numerical simulations and experiments. Compared with the traditional planar multirotor UAV, this novel orthogonal octo-rotor UAV features a compact structure with four horizontal main rotors for stability and thrust and four vertical auxiliary rotors for lateral movement. Additionally, the attitude and translation dynamics are decoupled with easy manipulations. To obtain a better hover performance, the flow field and hover performance of the UAV were analyzed with rotor spacing ratios i = D/L ranging from 0.55 to 0.9 (i = 0.55, 0.59, 0.63, 0.67, 0.71, 0.77, 0.83, 0.90) and a rotor speed ranging from 1500 to 2300 RPM. The results showed that a rotor spacing ratio of i = 0.55 achieves a better hover efficiency, increasing hover efficiency by 11.27% at 2000 RPM, where the maximal thrust increment is 3.92% and the power consumption decreased by 5.68% at the same time. Computational Fluid Dynamics (CFD) simulations were further validated by streamlines and velocity distributions. It indicated that the rotor interference from the outflow was decreased with the rotor spacing while the increasing rotor speed aggravated the influence of the auxiliary rotor on the downwash airflow of the main rotor. The potential benefit of the rotor interference from the main rotor and the auxiliary rotor improved the hover efficiency of the orthogonal octo-rotor UAV with a higher thrust increment, which offers it the unique capability of resisting wind gusts or even rotor failure, which will be validated with more field flight tests. Full article

Arctic Alaska is warming at twice the rate of the rest of the nation, severely impacting infrastructure built on permafrost. As winters warm, the effectiveness of thermosyphons used to stabilize foundations diminishes, increasing the risk of infrastructure failure. Because thermosyphons operate with the highest efficiency during winter cold snaps, studying the variation trends and patterns of winter cold snaps in Alaska is particularly important. To address this issue, this study analyzes the historical temperature data of four selected locations in Subarctic and Arctic Alaska, including Bethel, Fairbanks, Nome, and Utqiagvik. The winter cold snap is defined as a period when the average daily temperature drops below a specific site’s mean winter air temperature. The frequency, duration, and intensity of the winter cold snaps are computed to reveal their trends. The results indicate that the mean annual air temperature (MAAT) shows a warming trend, accompanied by sudden warming after 1975 for all study sites. The long-term average monthly air temperature also indicates that the most significant warming occurs in the winter months from December to March. While the frequencies of winter cold snaps remain relatively unchanged, the mean intensity and duration of cold snaps show a declining trend. Most importantly, the most intense cold snap during which the thermosyphons are the most effective is becoming much milder over time for all study sites. This study focuses specifically on the impact of changes in winter cold spells on thermosyphon effectiveness while acknowledging the complexity of other influencing factors, such as temperature differences, design features, coolant properties, and additional climatic parameters (e.g., wind speed, precipitation, and humidity). The data for this study were obtained from the NOAA NCEI website. The findings of this study can serve as a valuable reference for the retrofit or design of foundations and for decision making in selecting appropriate foundation stabilizing measures to ensure the long-term stability and resilience of infrastructure in permafrost regions. Moreover, the insights gained from this research on freeze–thaw dynamics, which are also relevant to black soils, align with the journal’s focus on sustainable soil utilization and infrastructure resilience. Full article