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Fault Detection Based on a Combined Approach of FA-CP-ELM with Application to Wind Turbine System
Wenxin Yu,Shoudao Huang,Junnian Wang 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.1
In this paper, a novel wind turbine (WT) fault detection method, based on the Partial Least Squares (PLS), Firefly Algorithm (FA), Chaos Map (CP) and Extreme Learning Machine (ELM), which is proposed and explained in detail. The proposed method includes two procedures: a WT mathematical model with PLS and a prediction model with FA-CP-ELM. Since the WT system is modeled as a system using PLS, the ELM has been optimized by the FA and CP to improve the predictive performance. Then, it’s calculated the residual between the mathematical model and the predicted model. If a fault occurs, the residual will increase accordingly and exceed the tolerance range. Hence, a fault can be detected quickly. To demonstrate the feasibility and effectiveness of the proposed approach, the wind turbine system is tested with a fault point set in this system. According to the results of the example, this proposed method is found to achieve better performance.
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Super-Twisting Sliding Mode Control Design for Cascaded Control System of PMSG Wind Turbine
Dinh Hieu Phan,ShouDao Huang 전력전자학회 2015 JOURNAL OF POWER ELECTRONICS Vol.15 No.5
This study focuses on an advanced second-order sliding mode control strategy for a variable speed wind turbine based on a permanent magnet synchronous generator to maximize wind power extraction while simultaneously reducing the mechanical stress effect. The control design based on a modified version of the super-twisting algorithm with variable gains can be applied to the cascaded system scheme comprising the current control loop and speed control loop. The proposed control inheriting the well-known robustness of the sliding technique successfully deals with the problems of essential nonlinearity of wind turbine systems, the effects of disturbance regarding variation on the parameters, and the random nature of wind speed. In addition, the advantages of the adaptive gains and the smoothness of the control action strongly reduce the chatter signals of wind turbine systems. Finally, with comparison with the traditional super-twisting algorithm, the performance of the system is verified through simulation results under wind speed turbulence and parameter variations.
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Super-Twisting Sliding Mode Control Design for Cascaded Control System of PMSG Wind Turbine
Phan, Dinh Hieu,Huang, ShouDao The Korean Institute of Power Electronics 2015 JOURNAL OF POWER ELECTRONICS Vol.15 No.5
This study focuses on an advanced second-order sliding mode control strategy for a variable speed wind turbine based on a permanent magnet synchronous generator to maximize wind power extraction while simultaneously reducing the mechanical stress effect. The control design based on a modified version of the super-twisting algorithm with variable gains can be applied to the cascaded system scheme comprising the current control loop and speed control loop. The proposed control inheriting the well-known robustness of the sliding technique successfully deals with the problems of essential nonlinearity of wind turbine systems, the effects of disturbance regarding variation on the parameters, and the random nature of wind speed. In addition, the advantages of the adaptive gains and the smoothness of the control action strongly reduce the chatter signals of wind turbine systems. Finally, with comparison with the traditional super-twisting algorithm, the performance of the system is verified through simulation results under wind speed turbulence and parameter variations.
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La Van Truong,ShouDao Huang,Vu Thi Yen,Pham Van Cuong 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.9
This paper proposed a novel adaptive tracking neural network with deadzone robust compensator for Industrial Robot Manipulators (IRMs) to achieve the high precision position tracking performance. In order, to deal the uncertainty, the unknown deadzone effect, the unknown dynamics, and disturbances of robot system, the Radial Basis function neural networks (RBFNNs) control is presented to control the joint position and approximate the unknown dynamics of an n-link robot manipulator. The online adaptive control training laws and estimation of thedead-zone are determined by Lyapunov stability and the approximation theory, so that the stability of the entire system and the convergence of the weight adaptation are guaranteed. In this controller, a robust compensator is constructed as an auxiliary controller to guarantee the stability and robustness under various environments such as the mass variation, the external disturbances and modeling uncertainties. The proposed control is the verified on a three-joint robot manipulators via simulations and experiments in comparison with PID and Neural networks (NNs) Control.
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Jian Gao,Chengxu Li,Wenjuan Zhang,Shoudao Huang 전력전자학회 2021 JOURNAL OF POWER ELECTRONICS Vol.21 No.12
This paper develops a method for permanent magnet (PM) temperature estimation in high power density permanent magnet synchronous machines (PMSMs) by considering magnetic saturation. Most of the previous methods in the literature are based on unsaturation. In this paper, the temperature estimation method of PMs is improved by adding a saturation coefficient. Once a machine is assembled, the inner and outer PM surfaces cannot be seen. Thus, it is impossible to realize visualization measurement of the permanent magnet temperature distribution. In this case, temperature sensors attached to the PM cam be used. However, the cost and robustness need to be considered. Therefore, in this paper, by solving a magnetic–thermal coupling finite element model, the temperature field distribution of a high power density PMSM is obtained. Then, an experimental platform is built to verify the model. Finally, the model is used to verify the reliability of the modified estimation method.
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