Electric power conversion system (EPCS), which consists of a generator and power converter, is one of the most important subsystems in a direct-drive wind turbine (DD-WT). However, this component accounts for the most failures (approximately 60% of the total number) in the entire DD-WT system according to statistical data. To improve the reliability of EPCSs and reduce the operation and maintenance cost of DD-WTs, numerous researchers have studied condition monitoring (CM) and fault diagnostics (FD). Numerous CM and FD techniques, which have respective advantages and disadvantages, have emerged. This paper provides an overview of the CM, FD, and operation control of EPCSs in DD-WTs under faults. After introducing the functional principle and structure of EPCS, this survey discusses the common failures in wind generators and power converters; briefly reviewed CM and FD methods and operation control of these generators and power converters under faults; and discussed the grid voltage faults related to EPCSs in DD-WTs. These theories and their related technical concepts are systematically discussed. Finally, predicted development trends are presented. The paper provides a valuable reference for developing service quality evaluation methods and fault operation control systems to achieve high-performance and high-intelligence DD-WTs.
Electric power infrastructure has recently undergone a comprehensive transformation from electromagnetics to semiconductors. Such a development is attributed to the rapid growth of power electronic converter applications in the load side to realize energy conservation and on the supply side for renewable generations and power transmissions using high voltage direct current transmission. This transformation has altered the fundamental mechanism of power system dynamics, which demands the establishment of a new theory for power system control and protection. This paper presents thoughts on a theoretical framework for the coming semiconducting power systems.
This study examines the development of the fluid and control technology of hydraulic wind turbines. The current state of hydraulic wind turbines as a new technology is described, and its basic fluid model and typical control method are expounded by comparing various study results. Finally, the advantages of hydraulic wind turbines are enumerated. Hydraulic wind turbines are expected to become the main development direction of wind turbines.
The magnitude and stability of power output are two key indices of wind turbines. This study investigates the effects of wind shear and tower shadow on power output in terms of power fluctuation and power loss to estimate the capacity and quality of the power generated by a wind turbine. First, wind speed models, particularly the wind shear model and the tower shadow model, are described in detail. The widely accepted tower shadow model is modified in view of the cone-shaped towers of modern large-scale wind turbines. Power fluctuation and power loss due to wind shear and tower shadow are analyzed by performing theoretical calculations and case analysis within the framework of a modified version of blade element momentum theory. Results indicate that power fluctuation is mainly caused by tower shadow, whereas power loss is primarily induced by wind shear. Under steady wind conditions, power loss can be divided into wind farm loss and rotor loss. Wind farm loss is constant at 3α(3α−1)R2/(8H2). By contrast, rotor loss is strongly influenced by the wind turbine control strategies and wind speed. That is, when the wind speed is measured in a region where a variable-speed controller works, the rotor loss stabilizes around zero, but when the wind speed is measured in a region where the blade pitch controller works, the rotor loss increases as the wind speed intensifies. The results of this study can serve as a reference for accurate power estimation and strategy development to mitigate the fluctuations in aerodynamic loads and power output due to wind shear and tower shadow.
The reliability and service life of wind turbines are influenced by the complex loading applied on the hub, especially amidst a poor external wind environment. A three-point elastic support, which includes the main bearing and two torque arms, was considered in this study. Based on the flexibilities of the planet carrier and the housing, a coupled dynamic model was developed for a wind turbine drive train. Then, the dynamic behaviors of the drive train for different elastic support parameters were computed and analyzed. Frequency response functions were used to examine how different elastic support parameters influence the dynamic behaviors of the drive train. Results showed that the elastic support parameters considerably influenced the dynamic behaviors of the wind turbine drive train. A large support stiffness of the torque arms decreased the dynamic response of the planet carrier and the main bearing, whereas a large support stiffness of the main bearing decreased the dynamic response of planet carrier while increasing that of the main bearing. The findings of this study provide the foundation for optimizing the elastic support stiffness of the wind turbine drive train.
Given the weak early degradation characteristic information during early fault evolution in gearbox of wind turbine generator, traditional singular value decomposition (SVD)-based denoising may result in loss of useful information. A weak characteristic information extraction based on µ-SVD and local mean decomposition (LMD) is developed to address this problem. The basic principle of the method is as follows: Determine the denoising order based on cumulative contribution rate, perform signal reconstruction, extract and subject the noisy part of signal to LMD and µ-SVD denoising, and obtain denoised signal through superposition. Experimental results show that this method can significantly weaken signal noise, effectively extract the weak characteristic information of early fault, and facilitate the early fault warning and dynamic predictive maintenance.
A new hierarchical parameter estimation method for doubly fed induction generator (DFIG) and drive train system in a wind turbine generator (WTG) is proposed in this paper. Firstly, the parameters of the DFIG and the drive train are estimated locally under different types of disturbances. Secondly, a coordination estimation method is further applied to identify the parameters of the DFIG and the drive train simultaneously with the purpose of attaining the global optimal estimation results. The main benefit of the proposed scheme is the improved estimation accuracy. Estimation results confirm the applicability of the proposed estimation technique.
Power maximization has always been a practical consideration in wind turbines. The question of how to address optimal power capture, especially when the system dynamics are nonlinear and the actuators are subject to unknown faults, is significant. This paper studies the control methodology for variable-speed variable-pitch wind turbines including the effects of uncertain nonlinear dynamics, system fault uncertainties, and unknown external disturbances. The nonlinear model of the wind turbine is presented, and the problem of maximizing extracted energy is formulated by designing the optimal desired states. With the known system, a model-based nonlinear controller is designed; then, to handle uncertainties, the unknown nonlinearities of the wind turbine are estimated by utilizing radial basis function neural networks. The adaptive neural fault tolerant control is designed passively to be robust on model uncertainties, disturbances including wind speed and model noises, and completely unknown actuator faults including generator torque and pitch actuator torque. The Lyapunov direct method is employed to prove that the closed-loop system is uniformly bounded. Simulation studies are performed to verify the effectiveness of the proposed method.
A brief description of the windmills from the second millennium BC to the Renaissance is presented. This survey is a part of several studies conducted by the authors on technology in the ancient world. The windmills are the first motor, other than human muscles, and are the ancestors of the modern wind turbines. Some authors’ virtual reconstructions of old windmills are also presented. The paper shows that the operating principle of many modern machines had already been conceived in the ancient times by using a technology that was more advanced than expected, but with two main differences, as follows: Similar tasks were accomplished by using much less energy; and the environmental impact was nil or very low. Modern designers should sometimes consider simplicity rather than the use of a large amount of energy.
The inevitable deterioration of the lubrication conditions in a gearbox in service can change the tribological properties of the meshing teeth. In turn, such changes can significantly affect the dynamic responses and running status of gear systems. This paper investigates such an effect by utilizing virtual prototype technology to model and simulate the dynamics of a wind turbine gearbox system. The change in the lubrication conditions is modeled by the changes in the friction coefficients, thereby indicating that poor lubrication causes not only increased frictional losses but also significant changes in the dynamic responses. These results are further demonstrated by the mean and root mean square values calculated by the simulated responses under different friction coefficients. In addition, the spectrum exhibits significant changes in the first, second, and third harmonics of the meshing components. The findings and simulation method of this study provide theoretical bases for the development of accurate diagnostic techniques.
Planetary transmission plays a vital role in wind turbine drivetrains, and its fault diagnosis has been an important and challenging issue. Owing to the complicated and coupled vibration source, time-variant vibration transfer path, and heavy background noise masking effect, the vibration signal of planet gear in wind turbine gearboxes exhibits several unique characteristics: Complex frequency components, low signal-to-noise ratio, and weak fault feature. In this sense, the periodic impulsive components induced by a localized defect are hard to extract, and the fault detection of planet gear in wind turbines remains to be a challenging research work. Aiming to extract the fault feature of planet gear effectively, we propose a novel feature extraction method based on spectral kurtosis and time wavelet energy spectrum (SK-TWES) in the paper. Firstly, the spectral kurtosis (SK) and kurtogram of raw vibration signals are computed and exploited to select the optimal filtering parameter for the subsequent band-pass filtering. Then, the band-pass filtering is applied to extrude periodic transient impulses using the optimal frequency band in which the corresponding SK value is maximal. Finally, the time wavelet energy spectrum analysis is performed on the filtered signal, selecting Morlet wavelet as the mother wavelet which possesses a high similarity to the impulsive components. The experimental signals collected from the wind turbine gearbox test rig demonstrate that the proposed method is effective at the feature extraction and fault diagnosis for the planet gear with a localized defect.
A brushless electrically excited synchronous generator (BEESG) with a hybrid rotor is a novel electrically excited synchronous generator. The BEESG proposed in this paper is composed of a conventional stator with two different sets of windings with different pole numbers, and a hybrid rotor with powerful coupling capacity. The pole number of the rotor is different from those of the stator windings. Thus, an analysis method different from that applied to conventional generators should be applied to the BEESG. In view of this problem, the equivalent circuit and electromagnetic torque expression of the BEESG are derived on the basis of electromagnetic relation of the proposed generator. The generator is simulated and tested experimentally using the established equivalent circuit model. The experimental and simulation data are then analyzed and compared. Results show the validity of the equivalent circuit model.
Monitoring of wind turbines under variable-speed operating conditions has become an important issue in recent years. The gearbox of a wind turbine is the most important transmission unit; it generally exhibits complex vibration signatures due to random variations in operating conditions. Spectral analysis is one of the main approaches in vibration signal processing. However, spectral analysis is based on a stationary assumption and thus inapplicable to the fault diagnosis of wind turbines under variable-speed operating conditions. This constraint limits the application of spectral analysis to wind turbine diagnosis in industrial applications. Although order-tracking methods have been proposed for wind turbine fault detection in recent years, current methods are only applicable to cases in which the instantaneous shaft phase is available. For wind turbines with limited structural spaces, collecting phase signals with tachometers or encoders is difficult. In this study, a tacholess order-tracking method for wind turbines is proposed to overcome the limitations of traditional techniques. The proposed method extracts the instantaneous phase from the vibration signal, resamples the signal at equiangular increments, and calculates the order spectrum for wind turbine fault identification. The effectiveness of the proposed method is experimentally validated with the vibration signals of wind turbines.