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Electrical faults can lead to transient and dynamic excitations of the electromagnetic generator torque in wind turbines. The fast changes in the generator torque lead to load oscillations and rapid changes in the speed of rotation. The combination of dynamic load reversals and changing rotational speeds can be detrimental to gearbox components. This paper shows, via simulation, that the smearing risk increases due to the electrical faults for cylindrical roller bearings on the high speed shaft of a wind turbine research nacelle. A grid fault was examined for the research nacelle with a doubly fed induction generator concept. Furthermore, a converter fault was analyzed for the full size converter concept. Both wind turbine grid connection concepts used the same mechanical drive train. Thus, the mechanical component loading was comparable. During the grid fault, the risk of smearing increased momentarily by a maximum of around 1.8 times. During the converter fault, the risk of smearing increased by around 4.9 times. Subsequently, electrical faults increased the risk of damage to the wind turbine gearbox bearings, especially on the high speed stage.

Offshore wind turbines have rapidly scaled up in recent years, with plans to construct turbines up to 22 MW in the next decade. However, the operations and maintenance (O&M) requirements for these ‘next‐generation turbines’ remain largely unknown. In this study, the total O&M costs are calculated, using a bench‐marked O&M model, for a hypothetical 10 MW turbine scenario using two drive train configurations, based on known failure rates of smaller turbines. The O&M costs of the 10 MW turbines are compared with those of existing 3 MW turbines in two case studies: a North Sea wind farm and an East Coast US wind farm. Overall, direct drive 10 MW turbines performed better depending on the site's climate conditions. The study indicated that the two‐stage drive train configuration may be more suitable for the US site than the North Sea, depending on the turbine's failure rate. The US site benefited from increased availability due to more favourable weather windows, resulting in lower lost revenue for the two‐stage configuration despite high transport costs. The study found that the failure rate of 10 MW offshore wind turbines in the North Sea with a two‐stage gearbox can increase by as much as 30% compared to the 3 MW failure rates without increasing direct O&M costs. These findings are crucial for the offshore wind energy industry, particularly for OEMs, developers and maintenance providers, as they provide insights into the required reliability for next generation turbines to reduce O&M costs compared to existing 3 MW turbines.

Even though wind energy is one of the most mature renewable technologies, it is in continuous development not only because of the trend towards larger wind turbines but also because of the development of new technological solutions. The gearbox is one of the components of the drive train in which the industry is concentrating more effort on research and development. Larger rotor blades lead to more demanding requirements for this component as a consequence of a higher mechanical torque and multiplication ratio (due to lower rotational speed of blades while the rotational speed on the generator side remains at similar values). In addition, operating conditions become increasingly demanding in terms of reliability, performance, and compactness. This paper analyses the different gearbox arrangements that are implemented by manufacturers of onshore wind turbines, as well as their market penetration (including different aspects that affect the design of the gearbox, such as drive train configuration and turbine size). The analysis carried out shows a clear convergence towards gearboxes with three stages. However, there is a noticeable diversity in the types of gears used, depending to a large extent on the preferences of each manufacturer but also on the nominal power of the wind turbine and drive train configuration.

The idea of indicative fault diagnosis based on measuring the wind turbine tower sound and vibration is presented. It had been reported by a wind farm operator that a major fault on the generator bearing causes shock and noise to be heard from the bottom of the wind turbine tower. The work in this paper was conceived to test whether tower top faults could be identified by taking simple measurements at the tower base. Two accelerometers were attached inside the wind turbine tower, and vibration data was collected while the wind turbine was in operation. Tower vibration signals were analyzed using Empirical Mode Decomposition and the outcomes were correlated with the vibration signals acquired directly from the generator bearings. It is shown that the generator bearing fault signatures were present in the vibrations from the tower. The results suggest that useful condition monitoring of nacelle components can be done even when there is no condition monitoring system installed on the generator bearings, as is often the case for older wind turbines. In the second part of the paper, acoustic measurements from a healthy and a faulty wind turbine are shown. The preliminary analysis suggests that the generator bearing fault increases the overall sound pressure level at the bottom of the tower, and is not buried in the background noise.

Feature extraction from nonlinear and non-stationary (NNS) wind turbine (WT) condition monitoring (CM) signals is challenging. Previously, much effort has been spent to develop advanced signal processing techniques for dealing with CM signals of this kind. The Empirical Wavelet Transform (EWT) is one of the achievements attributed to these efforts. The EWT takes advantage of Empirical Mode Decomposition (EMD) in dealing with NNS signals but is superior to the EMD in mode decomposition and robustness against noise. However, the conventional EWT meets difficulty in properly segmenting the frequency spectrum of the signal, especially when lacking pre-knowledge of the signal. The inappropriate segmentation of the signal spectrum will inevitably lower the accuracy of the EWT result and thus raise the difficulty of WT CM. To address this issue, an enhanced EWT is proposed in this paper by developing a feasible and efficient spectrum segmentation method. The effectiveness of the proposed method has been verified by using the bearing and gearbox CM data that are open to the public for the purpose of research. The experiment has shown that, after adopting the proposed method, it becomes much easier and more reliable to segment the frequency spectrum of the signal. Moreover, benefitting from the correct segmentation of the signal spectrum, the fault-related features of the CM signals are presented more explicitly in the time-frequency map of the enhanced EWT, despite the considerable noise contained in the signal and the shortage of pre-knowledge about the machine being investigated.

Traditional wind turbine drivetrain health assessment generally depends on feature extraction guided by expert experience and prior knowledge. However, the effectiveness of this approach is often limited when such knowledge is insufficient or when fault features are obscured by high levels of ambient noise. In response to these issues, this study proposes a new data-driven framework that combines intelligent frequency band identification with a deep learning architecture. In the proposed approach, vibration signals from the bearings are transformed into their spectral representation, and the frequency spectrum is divided into multiple frequency bands. The relative importance of each band is evaluated and ranked using XGBoost, enabling the selection of the most informative features and significant dimensionality reduction. A hybrid CNN–Transformer model is then employed to combine local feature extraction with global attention mechanisms for accurate fault classification. Experimental evaluations using two open-source datasets indicate that the proposed framework achieves high classification accuracy and rapid convergence, offering a robust and computationally efficient solution for wind turbine drivetrain fault diagnosis.

In the over-nominal wind speed region, the power output of a wind turbine is controlled by adjusting the blade pitch angle. A wind turbine exhibits nonlinear relations with varying wind speeds; therefore, designing a suitable pitch angle controller for the wind turbines is a significant engineering challenge. The current article primarily focuses on developing a Takagi–Sugeno fuzzy logic (TSFL) tuned PID pitch controller for a wind turbine connected to an electric generator through a 2-mass drive train. Further, the second stage presents a comparative analysis between optimized and Unoptimised power outputs from the permanent magnet synchronous generator. The Genetic Algorithm (GA) modifies the mutation rate and crossover point number. MATLAB/Simulink software validated the GA approach and produced superior results. Thus, the proposed GA-optimized controller better adjusts the wind turbine’s blade pitch angle at higher wind speeds than the unoptimized pitch controller.

The paper introduces new techniques to reduce the potential for pole-slipping induced by control systems and presents a low-cost pole-slipping detection and recovery scheme for magnetic drive-trains (MDTs). For the first time, the paper shows that a combination of electromagnetic and load-torque excitations which individually are not greater than the maximum coupling torque can initiate pole-slipping. For applications where acceleration feedback is unavailable, the motor-side inertia is virtually increased with a tracking differentiator to provide feedback of acceleration. Subsequently, controller design and parameter optimization are discussed. Experimental measurements on a custom test facility verify the presented principles that low-bandwidth controller designs with low inertia ratios can accommodate a wider range of on-load startup torque and load-torque disturbances without pole-slipping. To address overload issues, a pole-slipping detection method based on the kurtosis of electromagnetic torque and a recovery strategy based on converting the state of pole-slipping into that of on-load startup are presented. Experimental results demonstrate that detecting slip anomalies without load-side information, and recovery from pole-slipping without auxiliary mechanical devices are both feasible.

Mechanical loads considerably impact wind turbine lifetime, and a reduction in this load is crucial while designing a controller for maximum power extraction at below-rated speed (region II). A trade-off between maximum energy extraction and minimum load on the drive train shaft is a big challenge. Some conventional controllers extract the maximum power with a cost of high fluctuations in the generator torque and transient load. Therefore, to overcome the above issues, this work proposes four different integral synergetic control schemes for a wind turbine at region II using a two-mass model with a wind speed estimator. In addition, the proposed controllers have been developed to enhance the maximum power extraction from the wind whilst reducing the control input and drive train oscillations. Moreover, a terminal manifold has been considered to improve the finite time convergence rate. The effectiveness of the proposed controllers is validated through a 600 kW Fatigue, Aerodynamics, Structures, and Turbulence simulator. Further, the proposed controllers were tested by different wind spectrums, such as Kaimal, Von Karman, Smooth-Terrain, and NWTCUP, with different turbulent intensities (10% and 20%). The overall performance of the proposed and conventional controller was examined with 24 different wind speed profiles. A detailed comparative analysis was carried out based on power extraction and reduction in mechanical loads.

Due to the complexity of the process, there is no single solution for determining the motors, gearboxes and structures of a robot manipulator according to the desired dynamic performance while minimising both the deflections in the structure during the dynamic motion and total robot weight. The solution of this integrated drive-train and dynamic structural optimisation problem is generalised for three degrees of freedom (DOF) robot manipulator via Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to obtain the Pareto front of any desired robot manipulator overall conceptual design, including motors, gearboxes and thicknesses of the links. A flexible body dynamic simulation model was created in the MATLAB Simmechanics environment. The flexible bodies were defined via lumped parameter estimation method, which allows observation of the deflections in links during the dynamic motion. A library containing technical data related to motors and gearboxes was created to be utilised in the optimisation algorithm. The method accelerates the time-consuming iterative process for obtaining optimum conceptual design solutions for a dynamic system and allows for easy modification of design parameters and constraints. It also makes the algorithm suitable for different types of dynamic system designs.