Control
M. Mousavi; M. Ayati; M.R. Hairi-Yazdi; S. Siahpour
Abstract
Background and Objectives: In this paper, a novel linear parameter varying (LPV) model of a wind turbine is developed based on a benchmark model presented by Aalborg University and KK-electronic a/c. The observability and validity of the model are investigated using real aerodynamic data.Methods: In ...
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Background and Objectives: In this paper, a novel linear parameter varying (LPV) model of a wind turbine is developed based on a benchmark model presented by Aalborg University and KK-electronic a/c. The observability and validity of the model are investigated using real aerodynamic data.Methods: In addition, a robust fault detection and reconstruction method for linear parameter varying systems using second-order sliding mode observer is developed and implemented on the linear parameter varying model. The fault signal is reconstructed using a nonlinear term named equivalent output error injection during sliding motion and a proper transformation. The effect of uncertainties and incorrect measurements are minimized by employing an oriented method that requires solving a nonlinear matrix inequality. During numerical simulations, an actuator fault in the pitch system is considered and the performance of the method in fault reconstruction is investigated.Results: Wind speed range is considered from 14 m/s to 16 and it is regarded as a stochastic input exerting aerodynamic torque. Fast and accurate fault reconstruction happens in 0.6 seconds with less than one percent error. The observer performance is not affected by the fault and fault is estimated in 2.5 seconds with an error smaller than 2.48 percent.Conclusion: Results illustrate fast and accurate fault reconstruction and accurate state estimations in the presence of actuator fault.
Control
S. Khodakaramzadeh; M. Ayati; M. R. Haeri Yazdi
Abstract
Background and Objectives: Designing a terminal sliding mode observer (TSMO) in order to estimate the potential faults in a wind turbine with a doubly fed induction generator (DFIG) has been studied in previous research works. In this paper, a method for fault detection of a permanent magnet synchronous ...
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Background and Objectives: Designing a terminal sliding mode observer (TSMO) in order to estimate the potential faults in a wind turbine with a doubly fed induction generator (DFIG) has been studied in previous research works. In this paper, a method for fault detection of a permanent magnet synchronous generator (PMSG) wind turbine using a TSMO is developed.Methods: The wind turbine (WT) dynamic model including, blades, drive train, PMSG, maximum power capture controller, and pitch controller is linearized around its equilibrium point and is simulated in MATLAB Simulink. A PID controller is designed for capturing the maximum power from wind. Also, a PI controller is designed in order to control the pitch angle. In this research, the blade imbalance fault (BIF), which is due to the difference between turbine blades’ mass distribution, is investigated. This fault may happen over time and causes rotor mass imbalance that leads to vibrations in the generator’s shaft rotating speed. A fault detection system (FDS) is proposed using a terminal sliding mode observer in order to diagnose the BIF. Results: Using the designed TSMO, the estimation errors of not only measured states but also unmeasured states converge to zero in finite time. This leads to the fast action of the FDS before a failure happens. Using the proposed FDS, the states and fault are estimated such that the estimation errors of states and the fault converge to zero in 0.035 seconds.Conclusion: The convergence of state estimation errors to zero in finite time, which is verified by simulation results, satisfies the authors’ expectation that using TSMO, the estimation errors of both output and non-output states converge to zero in finite time.
