Figure - available from: Wind Energy
This content is subject to copyright. Terms and conditions apply.
Finite element generator model and solution results: (A) finite element generator model; (B) air gap magnetic density change curve with angle
Source publication
With the rapid development of wind turbine, the operational reliability of the main drive systems of wind turbine has received extensive attention. The wind turbine drive system is affected not only by random wind loads for a long time but also by the electromagnetic torque of the generator. Exploring the coupling behavior and mechanism is particul...
Similar publications
This paper presents the development and validation of a six degree of freedom (DOF) dynamic model of a two-stage parallel shaft gearbox, without flaws, which is able to determine the reaction of gearbox components to varying torque inputs and loads. The model utilises flexible shafts and gears rather than using a rigid assumption, to further unders...
Citations
... Thus, actual wind load and time-varied load excitation have gradually gained the attention of researchers. Chen et al. [20] presented a multi-source external environmental load modelling method and investigated resonance identification and dynamic responses under electromechanical coupling conditions. Wang et al. [21] studied the dynamic characteristics of both conventional and compact wind turbine gearboxes, including load-sharing and fatigue damage assessment. ...
Due to complex environmental factors, the gear transmission systems of wind turbines are continuously affected by large torque load excitation with periodic and random properties. This paper shares the load-sharing and dynamic characteristics of a herringbone planetary gear system applied in a wind turbine. The gear dynamic model is established using a typical lumped parameter method, in which the nonlinear transmission errors of the gear pairs and left and right-side coupling stiffness of the herringbone gears are included. With the help of the blade element momentum theory, the precise calculation of the hub load of the wind turbine, which is the external excitation of the gear system, is implemented, in which the wind shear, tower shadow, turbulent effect, and tip loss correction are taken into consideration. The nonlinear dynamic characteristics of the system are obtained using the Runge-Kutta method and then discussed. The results show that the turbulent effect plays a major role in the impact on the load-sharing characteristics, and a reasonable set of the support stiffness of rotational components can improve the load-sharing characteristics of the system. The purpose of this research is to provide some useful references in numerical modelling and methods for designers and researchers of wind turbine transmission systems.
... Uspensky [9] established a 16-degree-of-freedom torsional vibration nonlinear model of an oil extractor gear system with a generator, extracted the nonlinear magnetic field of the generator through finite element analysis, and applied the obtained perturbation moments to the gear system to analyze the nonlinear vibration of the system. Chen [10,11] established an electromechanical coupling model including a gear train dynamics model, generator finite element model, and voltage vector control by considering the time-varying meshing stiffness of the gears and generator magnetic characteristics, investigated the dynamics of the system, and proposed a resonant speed identification method under a variable speed and variable load model. Chen [12] established an integrated electric drive system model considering nonlinear excitation, analyzed the torsional vibration characteristics of the system during steady-state operation, and proposed a torsional vibration active suppression strategy; however, they did not consider motor control non-ideal factors and bearing stiffness. ...
... However, most previous studies analyzed the vibration characteristics of the electric drive system based on a simplified electromechanical coupled dynamics model or idealized the motor, disregarding the complex electromagnetic vibration and time-varying electromagnetic parameters of the drive motor [3-8, 15, 17, 18, 20, 21, 23, 25, 27]. Alternatively, the finite element method is used to obtain electromagnetic excitation; however, the finite element method is only a static simulation, which makes it difficult to strip the analysis of each factor and is not suitable for dynamic research [9,10,14,16,19,22]. Alternatively, the gear train is reduced to an inertial rotor, disregarding the mechanical vibration excitation inside the drivetrain [15,18,24,26]; or the parameter perturbations and nonlinear factors of the control system can be neglected [1,2,11,13,14,19,21,23] to simplify the control process of the motor. ...
... Because of the magnetic field saturation characteristics, the current operating point of the motor significantly affects the electromagnetic parameters. The electromagnetic torque components of the motor were analyzed according to (10). Equation (10) can be divided into the following parts: ...
The electromagnetic vibration of the motor and the mechanical vibration of the transmission mechanism are highly coupled in an electric drive system for an electric vehicle, complicating the positioning of the vibration source and suppress vibrations. To study the electromechanical coupling dynamic characteristics of the electric drive system under steady state, variable speed, and variable load operating conditions, a dynamic modeling method for a permanent magnet synchronous motor considering non-ideal factors was proposed, and a comprehensive electromechanical coupling nonlinear dynamic model of the electric drive system considering the electromagnetic nonlinearity of the permanent magnet synchronous motor, output nonlinearity of the control system, and meshing nonlinear factors of the gear transmission system was established. The dynamic response and electromechanical coupling dynamic characteristics of the electric drive system under steady-state, impact, and acceleration conditions were analyzed. The research results confirm that under the joint action of time current harmonics and space magnetic field harmonics, the frequency characteristics of the electromagnetic torque are more complex, and under steady-state conditions, electromagnetic and mechanical vibration excitations are further aggravated by the electromechanical coupling effect. In the phase current, there are clear electromechanical coupling frequency characteristics kfg1 ± fe and kfg2 ± fe (k = 1,2,3…); in the transient impact stage, the excitation system generates free vibration dominated by the 1st, 5th, and 12th modes; under acceleration conditions, the internal dynamic excitation of the gear system can easily excite the resonance of the high-frequency components of the system. Finally, compared with the motor-phase current test data, the accuracy of the mathematical modeling and dynamic characteristics were verified, providing a theoretical reference for mathematical modeling and dynamic characteristics research on electric vehicle drive systems.
... Zhang et al. 27 established a coupled electromechanical dynamic model of wind turbine drivetrain, explored the effect of the drivetrain system parameters on the current harmonics and dynamic response. Chen and colleagues 28,29 continued to establish a mechanical-electrical coupling nonlinear dynamic model of the wind turbine main drive system, and further revealed the influence of magnetic saturation coefficient and external variable-load excitation change on the dynamic characteristics. Form the above mentioned research, torsional vibration characteristics and the electromechanical coupling effect have been comprehensively investigated and discussed in wind turbine. ...
The drivetrain has an important impact on the stability and reliability of wind turbines operating in complex conditions, which belongs to the electromechanical coupling system. This work studies the two domains of research on drivetrain vibration analysis and control method in detail. The electromechanically coupled torsional vibration model is first established by considering the air gap magnetic field energy of the PMSG. Preliminary analysis in terms of Hamiltonian energy indicates that there is sufficient energy to sustain the exciting oscillation behaviors. Moreover, to reduce the vibration amplitude caused by wind speed or torsional stiffness, a novel adaptive fixed‐time control method is proposed to solve the issue of wind turbine drivetrain chaotic oscillation with uncertainties and disturbances. Furthermore, the proposed method not only can prevent the jumping and bifurcation of torsional vibration from happening, but also there are the advantages of the control scheme with high accuracy, fast convergence rate and strong robustness. For the fair comparison, three different control methods have been used to demonstrate the superiority of the control scheme by the simulation results. The research results can provide a theoretical basis for the parameter design and control of wind turbine drivetrain.
... Rezamand et al. 23 employed the kernel fuzzy C-means to categorize different operating conditions and analyzed the dynamics of wind turbine drivetrain bearings to predict the remaining useful life of faulty bearings. Chen et al. 24 established a mechanical-electrical coupling model of wind turbine drivetrain with variable speed and load, and revealed the relationship between dynamic characteristics and electromagnetic characteristics under different operating conditions. Tian et al. 25 analyzed the vibration time-frequency characteristics under different wear severity and torque conditions, and verified it by experiments. ...
Due to the random fluctuation of wind energy, the working conditions of the drivetrain have complex time‐varying characteristics. To deeply understand time‐varying dynamic behavior, a dynamic behavior method for normal and faulty drivetrain under multiple operating status is proposed. First, the working condition is divided into multiple working conditions by combining the control principle of the wind turbine with the K‐means clustering algorithm. Second, the torsional dynamics model of the drivetrain is established, solved, and verified. Finally, the dynamic behavior of normal and faulty drivetrain under multiple working conditions is analyzed. The dynamic behavior of drivetrain in normal and faulty states under different working conditions can be determined by the method proposed in this paper, which is affected by the system itself, external load, and the control strategy of the wind turbine. At the same time, the fault‐sensitive working condition range and the fault feature index under each working condition are determined. This research can provide an important theoretical basis for variable condition fault diagnosis.
... [2] constructed the coupling dynamic model of motor drive system and analyzed its vibration response characteristics. [21][22][23][24] studied the dynamic characteristics of thin plate structure under parametric excitation and external excitation. [25] studied the dynamic characteristics of flexible beam plate structure under fixed axis rotation and parametric excitation. ...
Aiming at the complex electromechanical coupling effect at the joints of RP (rotating parallel) flexible robot, the electromechanical coupling dynamics and vibration response characteristics driven by AC servo motor, as well as the dynamic starting characteristics of the motor are studied. The physical model including electromagnetic and mechanical system coupling is established, and the dynamic model of the whole system is derived based on the overall electromechanical coupling effect and Lagrange Maxwell equation. With the help of Matlab/Simulink, a virtual simulation platform is built to analyze the output speed characteristics of the motor drive end and the motion of the moving base. Finally, through the joint simulation of Matlab/Simulink dynamic simulation model and Adams/controls virtual prototype model, the vibration characteristics of flexible manipulator under electromechanical coupling are obtained. The simulation results show that the electromechanical coupling effect of the motor drive end has a significant impact on the dynamic characteristics of the flexible manipulator. The conclusions obtained are of great value for improving the chiral energy of flexible machinery.
... Zhang et al. [21] established a coupled electromechanical dynamic model of wind turbine drivetrain, explored the effect of the drivetrain system parameters on the current harmonics and dynamic response. Chen and Qin et al. [22] continued to establish a mechanical-electrical coupling nonlinear dynamic model of the main drive system in wind turbine, and further revealed the influence of magnetic saturation coefficient and external variable-load excitation change on the dynamic characteristics of the transmission system. Form the above mentioned researches, torsional vibration characteristics and the electromechanical coupling effect have been comprehensively investigated and discussed in wind turbine. ...
... In order to make the system obtain stronger robustness against unmodelled uncertainties and external disturbances in practical applications, and satisfy the sliding condition. The switching control law sw u can be described as Remark 3. In order to overcome the structural complexity of the uncertainties and disturbances, the above-proposed adaptive update law is utilized to estimate the upper bounds of the system lumped uncertainty in Eq. (22). It is worth mentioning that the adaptive law can not only make the tracking error converge to zero rapidly, but also obtain a better rate of convergence for the estimated parameters 0 a ， 1 a and 2 a .Furthermore, as for the upper bound of the system uncertainty, the prior knowledge of the adaptive tuning law does not require. ...
The drivetrain is the major module of wind turbines, which is a typical electromechanical coupling system and has an important impact on the stability and reliability of wind turbines operating in complex conditions. On this background, this work studies the torsional vibration characteristics analysis and control of the wind turbine drivetrain with the variation of internal parameter and external excitation. The electromechanically coupled torsional vibration model for wind turbine drivetrain is first established by taking into account the air gap magnetic field energy of the PMSG. Preliminary analysis in terms of Hamiltonian energy indicates that there is sufficient energy to sustain the exciting oscillation behaviors. Moreover, in order to reduce the vibration amplitude and suppress chaos motion caused by wind speed or torsional stiffness, a novel adaptive fixed-time control method is proposed to solve the issue of chaotic oscillation control for wind turbine drivetrain with uncertainties and disturbances. Furthermore, the proposed method not only can prevent the jumping and bifurcation of torsional vibration from happening, but also the advantage of the control scheme with high accuracy, fast convergence rate and strong robustness can be validated by the fair comparison of four different control methods. The research results can provide a theoretical basis for the parameter design and control of wind turbine drivetrain.
As high-speed, heavy-load, and high-power density are realized in hybrid electric vehicle (HEV), the electromechanical coupling inherent vibration of electromechanical composite transmission (EMT) system becomes the bottleneck in this study area. To reduce vibration, the analysis of inherent vibration characteristics of EMT system and parameter influencing law of mechanical-electro-magnetic parameter is key. Here, an electromechanical coupling dynamics model is established considering multi-source excitations, and the inherent vibration model is obtained. Based on this inherent vibration model, firstly, the inherent vibration frequency and mode with and without electromagnetic effect of EMT system are studied and summarized. Secondly, the resonance speed of EMT system is identified based on Campbell diagram and modal energy method. Thirdly, the inherent frequency trajectory and variation law affected by PMSM parameters (inner power factor angle (IPFA), armature current, stator-rotor eccentricity) and the mode mutations phenomenon of inherent frequency trajectory caused by PG parameter (connecting, gear meshing, and bearing support stiffness) are studied. This study results provide a reference for the modeling and analysis of inherent vibration characteristics, as well as the design and optimization of mechanical-electro-magnetic parameter of EMT system.
With the rapid development of science and technology, automatic control systems have been applied in more and more fields. At the same time, the requirements for the stability, accuracy, response speed, and self-regulation ability of the system are also increasing. In the wood processing industry, the heating system of factories is an important link to ensure normal production. Therefore, in order to further improve the production efficiency of the wood processing industry and enhance the stability and controllability of the heating system in wood processing production, this article takes into account the delay and coupling effects in the rapid heating process, and combines fuzzy control with temperature control to study and establish a woodworking thermal mechanical coupling model based on fuzzy control algorithm. The results show that compared with traditional PID control, fuzzy control has advantages such as short response time, small overshoot, high steady-state accuracy, fast steady-state recovery, and good dynamic and static performance. Under the condition of rapid heating, the existence of delay effect weakens the effect of Thermal shock, while the coupling effect not only affects the propagation of thermoelastic waves in the elastic body, but also weakens the weakening effect of delay effect on Thermal shock to a certain extent.
Structural loads vibration is a critical problem in WTs (wind turbines) due to many reasons such as the complicated dynamics, structural coupling and diversity of the driving sources such as gravitational, inertial, aerodynamic and operational loads. These loads cause pressures forces along the span of the blade and alternate bending moments of the chord-wise and flap-wise axes per revolution, thus leading to fatigue loads in WT components. The accumulation of these loads over time cause degradation and lifespan reduction of the overall system. Monitoring, analysis of the dynamics loads and mitigation of their impact are a straightforwardly challenge in modern WTs. In this regard, this article explores a deep research on dynamics loads mitigation for WTs applications. In the first time, baseline control of WTs, based on electromagnetic torque and collective pitch angle control (CPC), is presented. Thereafter, the paper presents the vibration control methods including passive, active and semi-active controls. The control based on the active method is economic, reliable and the generated forces are based on the control algorithms. This control includes drive-train damper, tower damper and individual pitch angle control (IPC). Subsequently, the various techniques and control strategies to implement these vibration control methods are given out and discussed in detail under various operating conditions such as wind speed fluctuation, high non-linearity, un-modeled dynamics, perturbations, disturbances … etc. These methods cover Disturbance accommodation control (DAC), Model Predictive Control (MPC), combined feed-forward and feedback control, robust and multi-variable control, and adaptive control. It can be stated that DAC allows to improve loads rejection under spatial varying wind, while that MPC allows to optimize a cost function containing a set of WTs objectives. Combined feed-forward and feedback control allows to actuate the WT in anticipation of the incoming disturbances. Multivariable and robust controls allow to accomplish WTs objectives under disturbances and uncertainties. Adaptive control allows to adjust its parameters to the WTs operating conditions under un-modeled dynamics and uncertainties. The paper finishes by conclusions and perspectives.
