Sensorless small wind turbine with a sliding-mode observer for water heating applications

Author(s):  
Jagath Sri Lal Senanayaka ◽  
Hamid Reza Karimi ◽  
Kjell G. Robbersmyr
2018 ◽  
Vol 41 (6) ◽  
pp. 1504-1518 ◽  
Author(s):  
Mostafa Rahnavard ◽  
Moosa Ayati ◽  
Mohammad Reza Hairi Yazdi

This paper proposes a robust fault diagnosis scheme based on modified sliding mode observer, which reconstructs wind turbine hydraulic pitch actuator faults as well as simultaneous sensor faults. The wind turbine under consideration is a 4.8 MW benchmark model developed by Aalborg University and kk-electronic a/s. Rotor rotational speed, generator rotational speed, blade pitch angle and generator torque have different order of magnitudes. Since the dedicated sensors experience faults with quite different values, simultaneous fault reconstruction of these sensors is a challenging task. To address this challenge, some modifications are applied to the classic sliding mode observer to realize simultaneous fault estimation. The modifications are mainly suggested to the discontinuous injection switching term as the nonlinear part of observer. The proposed fault diagnosis scheme does not require know the exact value of nonlinear aerodynamic torque and is robust to disturbance/modelling uncertainties. The aerodynamic torque mapping, represented as a two-dimensional look up table in the benchmark model, is estimated by an analytical expression. The pitch actuator low pressure faults are identified using some fault indicators. By filtering the outputs and defining an augmented state vector, the sensor faults are converted to actuator faults. Several fault scenarios, including the pitch actuator low pressure faults and simultaneous sensor faults, are simulated in the wind turbine benchmark in the presence of measurement noises. Simulation results show that the modified observer immediately and faithfully estimates the actuator faults as well as simultaneous sensor faults with different order of magnitudes.


2016 ◽  
Vol 753 ◽  
pp. 052031 ◽  
Author(s):  
Jagath Sri Lal Senanayaka ◽  
Hamid Reza Karimi ◽  
Kjell G. Robbersmyr

2020 ◽  
pp. 107754632092627
Author(s):  
Seyedeh Hamideh Sedigh Ziyabari ◽  
Mahdi Aliyari Shoorehdeli ◽  
Madjid Karimirad

In this article, a novel robust fault estimation scheme to ensure efficient and reliable operation of wind turbines has been presented. Wind turbines are complex systems with large flexible structures that work under very turbulent and unpredictable environmental conditions for a variable electrical grid. The proposed observer-based estimation scheme consists of a set of possible faults affecting the dynamics, sensors, and actuators of wind turbines. First, the pitch and drivetrain system faults occur simultaneously with process and sensor disturbances that are called unknown input signals. Second, through a series of coordinate transformations, the faulty subsystem is decoupled from the rest of the system. The first subsystem is affected by unknown inputs, and the second one is affected by faults. A reduced-order unknown input observer is designed to reconstruct states accurately, whereas a reduced-order sliding mode observer is designed for the second subsystem such that it is robust against unknown inputs and faults. Moreover, the reduced-order unknown input observer guarantees the asymptotic stability of the error dynamics using the Lyapunov theory method and completely removes unknown inputs; on the other hand, the reduced-order sliding mode observer is designed to reconstruct faults for the faulty subsystem accurately. Until now, authors only focused on an unknown input signal in the dynamics of the system, especially in nonlinear systems. The estimated fault will be adequate to accommodate the control loop, and sufficient conditions are developed to guarantee the stability of the state estimation error. In the next step, to figure the effectiveness of the proposed approach, a wind turbine benchmark system model is considered with faults and unknown inputs scenarios. The simulation results are used to validate the robustness of the proposed algorithms under noise conditions, and the results show that the algorithm could classify faults robustly.


Processes ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 54
Author(s):  
Vicente Borja-Jaimes ◽  
Manuel Adam-Medina ◽  
Betty Yolanda López-Zapata ◽  
Luis Gerardo Vela Valdés ◽  
Luisana Claudio Pachecano ◽  
...  

A fault detection and isolation (FDI) approach based on nonlinear sliding mode observers for a wind turbine model is presented. Problems surrounding pitch and drive train system FDI are addressed. This topic has generated great interest because the early detection of faults in these components allows avoiding irreparable damage in wind turbines. A fault diagnosis strategy using nonlinear sliding mode observer banks is proposed due to its ability to handle model uncertainties and external disturbances. Unlike the reported solutions, the solution approach does not need a priori knowledge of the faults and considers system uncertainty. The robustness to disturbances, uncertainties, and measurement noise is shown in the dynamic of the generated residuals, which is sensible to only one kind of fault. To show the effectiveness of the proposed FDI approach, numerical examples based on a wind turbine benchmark model, considering closed loop applications, are presented.


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