The blades directly affect the safety and power generation efficiency of the wind turbines. With the blade size increases, the reliable modal identification becomes important for vibration-based health monitoring. Although operational modal analysis (OMA) technique has been used in condition monitoring for the wind turbine blades, most existing studies focus on investigating a specific single method or under ideal excitation conditions. To overcome this limitation, this study takes the IEA-15MW large wind turbine blade as the research object and compares three OMA methods through numerical simulations, namely covariance-driven stochastic subspace identification (SSI-COV), frequency domain decomposition (FDD), and poly-reference least squares complex frequency domain (PolyMAX). The performance of the modal parameter identification methods is evaluated with respect to different sensor layouts, blade–tower coupling conditions, and environmental excitations. The results indicate that sparse sensor deployment cannot reliably identify the damage-sensitive high-order and complex modes. A nine-channel layout concentrated near second-order deformation regions significantly improves the identification of second-order flapwise frequencies and controls the average error of the first six modes within 3%. PolyMAX shows the best identification stability under different numbers and layouts of the sensors. Blade–tower coupling changes the blade modal characteristics and increases identification difficulty. Under this condition, FDD can still identify both low-order and high-order modes with good stability. Under different real wind conditions, the increasing wind speed causes the aerodynamic load to deviate from the white noise assumption, generally leading to fluctuations in the identification errors, with relatively large local errors occurring at certain medium and high wind speeds. Overall, the three OMA methods show different advantages under different identification conditions. PolyMAX shows the best stability under different sensor layouts and performs best when wind speed increases in the coupled wind turbine model, indicating that it is the most suitable for the actual complex coupling effects and environmental conditions. This research hopefully provides a basis for the subsequent engineering application of vibration-based modal identification of large offshore blades.
Vibration monitoring and health assessment of rotor blades using the blade tip-timing (BTT) method has gained increasing attention in turbine machinery. Identifying synchronous vibration parameters identification of rotor blades can help prevent high-cycle fatigue failure and can also evaluate blade status, such as vibration stress reconstruction and modal identification. However, during acceleration and deceleration, rotor blades passing through synchronous are affected by transient response. This leads to significant errors in the blade vibration parameters identified based on the steady-state response fitting method. To improve the accuracy of blade vibration parameters identification under transient response conditions, the transient vibration displacement response equation of rotor blades during synchronous vibration is derived based on the BTT method monitoring. A whole domain transient response fitting method is proposed by replacing the Faddeeva function with the imaginary error function. To reduce computation time and improve the identification efficiency, an improved whole domain transient response fitting (IWDTRF) method is developed by combining the advantages of Algorithm 916. The IWDTRF method enables engine order analysis by monitoring blade vibration displacement with only a single sensor based on the BTT method. Simulations and experiments demonstrate that the blade synchronous vibration parameters identified by the proposed method are more accurate and stable, and further confirm its effectiveness for transient vibration parameter identification under variable acceleration conditions, making it suitable for identifying synchronous vibration parameters of rotor blades under actual engine operation conditions.
Sanqun Ren, Wei Zhao, Qing-Jun Zhao et al.· Journal of Engineering For G...· 0 citations
Vibration response analysis constitutes a pivotal approach for crack monitoring and early warning of damage identification in wind turbine blades. Traditional data-driven methods, however, demonstrate marked deficiencies in identification accuracy and generalization capability. To mitigate these issues, a method for crack damage identification in wind turbine blades is proposed, grounded in Physics-Informed Neural Networks (PINNs). Initially, utilizing a scaled-down test platform for doubly fed wind turbines, simulation experiments on blade cracks were executed. Vibration data were amassed under varying crack locations and lengths to scrutinize the intrinsic relationship between crack characteristics and the three-dimensional vibration response of the blade root bearing pedestal. Subsequently, leveraging the rotating cantilever Euler–Bernoulli beam model, the physical correlation between cracks and vibrations was dissected, and a physical information constraint model was formulated. This model was then amalgamated with a GRU-Transformer network to establish a PINN model tailored for crack damage identification. Ultimately, the model underwent testing and validation utilizing experimental data. The outcomes reveal that, in comparison to traditional data-driven models, the PINN model exhibits superior accuracy and precision in crack identification and localization, along with exceptional generalization capability and noise resilience. This research provides a novel technical pathway for enhancing the intelligence level of health monitoring for wind turbine units and holds substantial engineering significance for achieving precise condition assessment and early fault warning.
Min Wang, G. Qin, Xiaofei Zhang· Machines· 0 citations
Wind energy, as a major contributor to the renewable energy sector, is receiving increasing attention to meet the growing demand for low-carbon electricity. The performance of a wind turbine can be significantly affected by various types of damage in its components, particularly the rotor blades. If left undetected, damage can develop rapidly under harsh operating conditions, potentially leading to severe failure, reduced power output, and considerable economic losses. However, the anisotropic properties and curved geometry of composite wind turbine blades make conventional time-domain methods less effective for reliable damage identification and localisation. To address this challenge, a methodology is developed that integrates deep learning with ultrasonic guided wave data analysis to enable accurate detection and classification of blade damage. An extensive numerical investigation is conducted, considering a range of damage locations and sizes across the blades. The samples are excited using a five-cycle Hanning-modulated sinusoidal pulse, and the structural responses at selected sensing positions are captured and converted into time-frequency representations to reveal more damage-sensitive features. The image-like data are then analysed using a custom-designed deep learning model to categorise and localise different damage cases. The results demonstrate a high level of accuracy in detecting both the location and severity of damage, indicating the strong potential of this approach for large-scale application in smart structural health monitoring of wind turbines.
Farbod Dadashbaki, S. Sikdar, D. D. Mandal et al.· e-Journal of Nondestructive...· 0 citations
This research is based on the results of a mechanical loadmonitoring campaign of a prototype wind turbine at an experimental wind farm, where an unexpected blade vibration was detected. This prototype belongs to Nordex. This vibration in the edgewise direction had a frequency of 4P (P=rotor rotation frequency) and caused a significant increase in blade loads. No previous information was found in the literature. Therefore, this research was initiated to understand this wind turbine dynamics problemobserved in the field and not reproduced in existing aeroelastic simulation models. In order to solve the problem of the blade vibration at frequency 4P in the edgewise direction (hereafter referred to as the vibration), several analyses of the Campbell diagrams of this wind turbine were carried out. On the one hand, the Campbell diagram in the local coordinate system of the blade showed a crossing between the 4P operating harmonic and the natural frequency of the blade edge near the nominal rotor speed. However, there was no real excitation at this 4P frequency that could produce such a resonance. On the other hand, the Campbell diagram in the fixed nacelle coordinate system showed a crossing between the 3P operating harmonic and the first rotor edgewise backward whirling mode, which was also close to the nominal rotor speed. In this case, there could be a real excitation at the 3P frequency which would create such a resonance. Furthermore, the operating point coincided with that observed in the field during the vibration. However, this resonance was not observed in the aeroelastic simulations. Another very important finding was that the data measured during the vibration showed a relation between the yaw system motion at a frequency of 3P and the blade vibration at a frequency of 4P. The amplitude of this yaw motion was up to 0.4 degrees. A new yaw system model was developed in the aeroelastic code to reproduce the dynamics observed during the vibration. Firstly, a basic spring-damper model was created. This model reproduced the vibration of the blade and made it possible to observe its sensitivity to various aspects such as the flexibility of the yaw system and different environmental conditions such as tower shadow, flow slope, vertical profile, wind direction, and turbulence. However, this newmodel did not accurately reproduce the relation observed in the field. These results were previously published in the journal Renewable Energy (Q1, according to JCR 2024), 227, 120503 (2024) [1]) and are included here as part of this thesis. Following the research, an advanced model of the yaw system was created in the aeroelastic code to incorporate flexibility and backlash. This model was first developed in Bladed and Alaska aeroelastic codes. It was found that the vibration was very sensitive to the backlash of the yaw system, reproducing the relation observed in the field. This new model allowed new control functions to be developed to reduce the vibration. This work was presented at the Bladed User Conferences in Hamburg (Germany) in October 2023. This new knowledge has led to new vibration control functions, which are protected by the following patents, which can be found in the appendices A and B of this document: • Patent A: “Yaw system damping at frequency 3P”. • Patent B: “Vibration triggered speed limitation”. During this research it was found that the kinematic and dynamic behaviour of the first rotor edgewise backward whirling mode was difficult to understand. Therefore, in order to gain an advanced understanding of its behaviour, a model was created to obtain representations that would help to understand its kinematic and dynamic behaviour. Representations of the following variables were obtained: • Modal displacements of the modal vectors of the first rotor edgewise backward whirling mode in the rotor reference frame. • The trajectories of the centre of gravity of the first rotor edgewise backward whirling mode in the nacelle reference frame. • The velocities of the centre of gravity of the first rotor edgewise backward whirling mode in the nacelle reference frame. • The inertial forces of the first rotor edgewise backward whirling mode in the inertial reference frame. This result was also presented at the european academy of wind energy conferences in Florence (Italy) in May 2024, and published in the corresponding Journal of Physics: Conference Series. 2767 052015 (2024) [2]. As wind turbine technology advances, new challenges arise, such as the need to design longer blades and thinner towers made of different materials to improve cost efficiency. As a result, system eigenfrequencies are lower and there is a higher probability of resonance between system modes and operational harmonics, which can lead to increased vibration amplitudes and loads on the wind turbine. For this reason, a study was initiatedwith the aim of contributing to the existing literature by performing a sensitivity analysis of tower and yaw system configurations, with particular focus to the comparison of steel and concrete towers and rigid and flexible yaw systems, with and without backlash. It was observed that the vibration was higher in the steel tower than in the concrete tower. Itwas also observed that the flexible yawsystem with backlashwasmore sensitive than the flexible one without backlash. And the latter more than the rigid one. Part of this research has led to a journal article currently under review, where the sensitivity of the 4P blade vibration to tower flexibility and yaw system behaviour is analysed in detail [3]. In conclusion, this research improves the state of the art and the understanding of the operational edgewise backward whirling mode resonance, allowing the optimisation of design rules and the development of control functions to mitigate these vibrations.
This research stems from the problem that adding unbalance mass to a rotating shaft alters system vibration characteristics, a phenomenon that remains insufficiently quantified in small-scale rotating engines. This study aims to analyze the effects of variations in mass position, radial distance, and rotational speed on the vibration characteristics of a small-scale engine using combined time-domain and frequency-domain approaches. A quantitative experimental design was conducted across 27 treatment combinations, evaluating mass distances (5-25 cm) and speeds up to 860 rpm (14.33 Hz). Data acquisition utilized an accelerometer-microcontroller setup, analyzed via peak acceleration, RMS, and FFT methods. Results show a direct proportional relationship between mass radial distance and vibration amplitude, with the highest response observed at a 25 cm load distance and 860 rpm. The y1-axis exhibited the highest acceleration and RMS values, identifying it as the most sensitive measurement axis for condition monitoring. FFT analysis revealed dominant spectral peaks at the fundamental shaft rotational frequency (approximately 14.3 Hz at 860 rpm), accompanied by sub-synchronous and harmonic components induced by mass imbalance. In conclusion, vibration response in small-scale engines is heavily governed by mass location and rotational speed, underscoring the necessity of strategic sensor orientation for accurate fault detection.
Salman Salman, I. Okariawan, P. D. Setyawan· Jurnal POLIMESIN· 0 citations
This study investigates the dynamic behaviour of an onshore wind turbine tower throughout the entire assembly process, with particular emphasis on the challenge of identifying closely spaced modes in operational modal analysis. A comprehensive measurement campaign, involving up to 23 accelerometers, was conducted to capture vibration responses under environmental excitation across seven construction stages. Modal parameters and their associated uncertainties were identified using the covariance-driven stochastic subspace identification (SSI-COV) within a robust operational modal analysis scheme. A novel uncertainty-based mode selection approach was introduced and applied to reliably extract the modes of the first two bending mode pairs. Additionally, a bending mode indicator was developed to assess the purity of the identified bending shapes. The results show that the natural frequencies of the first bending mode pair decrease continuously as construction progresses. A similar trend was observed for the fore-aft mode of the second bending mode pair, while the side-to-side mode remained largely unaffected. Modal contribution analysis reveals that, particularly in the later assembly stages, the structural dynamics are dominated by the first bending mode pair. These findings highlight the effectiveness of the uncertainty-based mode selection framework and provide new insights into the dynamics of wind turbine towers during assembly. The results support model validation for structural health monitoring and may inform future design and construction practices.
Leon Liesecke, Clemens Jonscher, Benedikt Hofmeister et al.· Journal of Civil Structural...· 0 citations
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