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Author: search.filters.author.RAJENDRAN, R.Subject: search.filters.subject.Modified Newton Rapshon

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    Second order ISMC for variable speed wind turbine
    (2013) RAJENDRAN, R.; Jena, D.
    In this paper, a nonlinear controller is designed for variable speed wind turbine (WT) where the dynamics of the WT is derived for single mass model. The main aim of the controller is to extract the optimum power capture from the wind and minimize the transient load on low speed shaft. Modified Newton Rapshon (MNR) is used to estimate the effective wind speed and the optimal rotor speed is derived from it. The controller is used to track the optimal rotor speed by adjusting the generator torque which is acting as a control input to the WT. Existing controllers such as Nonlinear static state feedback with estimator (NSSFE) and Nonlinear dynamic state feedback with estimator (NDSFE) are unable to track the WT dynamics and introduces more transient on drive trains. In order to overcome the above drawbacks a nonlinear controller i.e. sliding mode control with integral action (ISMC) is used. In this paper an ISMC with MNR based wind speed estimator is used to control the single mass WT. The result shows the significance improvement in proposed controllers compared with NSSFE and NDSFE. © 2013 IEEE.
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    Nonlinear control of a wind turbine based on nonlinear estimation techniques for maximum power extraction
    (Taylor and Francis Inc. 325 Chestnut St, Suite 800 Philadelphia PA 19106, 2016) RAJENDRAN, R.; Jena, D.
    This work proposes nonlinear estimators with nonlinear controllers, for variable speed wind turbine (VSWT) considering that either the wind speed measurement is not available or not accurate. The main objective of this work is to maximize the energy capture from the wind and minimizes the transient load on the drive train. Controllers are designed to adjust the generated torque for maximum power output. Estimation of effective wind speed is required to achieve the above objectives. In this work the estimation of effective wind speed is done by using the Modified Newton Rapshon (MNR), Neural Network (NN) trained by different training algorithms and nonlinear time series based estimation. Initially the control strategies applied was the classical ATF (Aerodynamic torque feed forward) and ISC (Indirect speed control), however due their weak performance and unmodeled WT disturbances, nonlinear static and dynamic feedback linearization techniques with the above wind speed estimators are proposed. © 2016 Taylor and Francis Group, LLC.