Wind Turbine Benchmark System Research Articles

Active fault tolerant control using zonotopic techniques for linear parameter varying systems: Application to wind turbine system

This paper deals with the design of an Active Fault Tolerant Control (AFTC) approach for polytopic uncertain Linear Parameter-Varying (LPV) systems subject to uncertainties and actuator faults. First, a fault estimation method is developed by integrating robust observer design with zonotopic techniques. The proposed observer is developed using L∞ norm to attenuate the effects of the uncertainties and to improve the accuracy of the estimation. Then, an AFTC strategy is used to compensate actuator fault effect and maintain system stability. Finally, the effectiveness of the proposed method is demonstrated by a case study on a 4.8MW wind turbine benchmark system.

Robust fault estimation of a blade pitch and drivetrain system in wind turbine model

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.

Robust Neural Network Fault Estimation Approach for Nonlinear Dynamic Systems With Applications to Wind Turbine Systems

In this paper, a robust fault estimation approach is proposed for multi-input and multioutput nonlinear dynamic systems on the basis of back propagation neural networks. The augmented system approach, input-to-state stability theory, linear matrix inequality optimization, and neural network training/learning are integrated so that a robust simultaneous estimate of system states and actuator faults are achieved. The proposed approaches are finally applied to a 4.8 MW wind turbine benchmark system, and the effectiveness is well demonstrated.

Parameter-varying modelling and fault reconstruction for wind turbine systems

In this paper, parameter-varying technique is firstly addressed for modelling a 4.8 MW wind turbine system which is nonlinear in essence. It is worthy to point out that the proposed parameter-varying model is capable of describing a nonlinear real-time process by using real-time system parameter updating. Secondly, fault reconstruction approach is proposed to reconstruct system component fault and actuator fault by utilizing augmented adaptive observer technique with parameter-varying. Different from the offline tuning adaptive scheme, the proposed adaptive observer includes adaptive tuning ability to online adjust the observer based on varying parameter. The effectiveness of the proposed parameter-varying modelling and fault reconstruction methods is demonstrated by using a widely-recognized 4.8 MW wind turbine benchmark system.

Takagi–Sugeno Fuzzy Model Based Fault Estimation and Signal Compensation With Application to Wind Turbines

In response to the high demand of the operation reliability by implementing real-time monitoring and system health management, a robust fault estimation and fault-tolerant control approach is proposed for Takagi–Sugeno fuzzy systems in this study, by integrating the augmented system method, unknown input fuzzy observer design, linear matrix inequality optimization, and signal compensation techniques. Specifically, a fuzzy augmented system method is used to construct an augmented plant with the concerned faults and system states being the augmented states. An unknown input fuzzy observer technique is thus utilized to estimate the augmented states and decouple unknown inputs that can be decoupled. A linear matrix inequality approach is further addressed to ensure the global stability of the estimation error dynamics and attenuate the influences from the unknown inputs that cannot be decoupled. As a result, the robust estimates of the concerned faults and system states can be obtained simultaneously. Based on the fault estimates, a signal compensation scheme is developed to remove the effects of the faults on the system dynamics and outputs, leading to a stable dynamic satisfying the expected performance. Finally, the effectiveness of the proposed Takagi–Sugeno model based fault estimation and signal compensation algorithms is demonstrated by a case study on a 4.8-MW wind turbine benchmark system.

An LPV modelling and fault diagnosis in wind turbine benchmark system

In order to keep away wind turbines from catastrophic conditions due to sudden breakdowns, it is important to detect faults as soon as possible. For diagnosis, a model-based approach is chosen. There are many works that use this fault detection design, but the majority of them consider this system as a linear time invariant (LTI) model. The objective of this paper is, first, to find an LPV model of the system using the subspace identification technique of linear parameter-varying (LPV). Second, we focus on fault diagnosis based on residual generation which is obtained as a comparison between the measured variable and the estimated one using this LPV model. In this work, a benchmark of a wind turbine case is proposed with six predefined faults (three sensor fault scenarios, two actuator fault scenarios and a system fault scenario).

An LPV modelling and fault diagnosis in wind turbine benchmark system

In order to keep away wind turbines from catastrophic conditions due to sudden breakdowns, it is important to detect faults as soon as possible. For diagnosis, a model-based approach is chosen. There are many works that use this fault detection design, but the majority of them consider this system as a linear time invariant (LTI) model. The objective of this paper is, first, to find an LPV model of the system using the subspace identification technique of linear parameter-varying (LPV). Second, we focus on fault diagnosis based on residual generation which is obtained as a comparison between the measured variable and the estimated one using this LPV model. In this work, a benchmark of a wind turbine case is proposed with six predefined faults (three sensor fault scenarios, two actuator fault scenarios and a system fault scenario).

Global wind turbine FTC via T-S fuzzy modelling and control

This paper presents a new approach to active sensor fault-tolerant control (FTC) for offshore wind (OSW) turbine systems operating in the low wind speed region described via Takagi-Sugeno (T-S) multiple models. The FTC strategy is designed in such way that aims to maintaining nominal wind turbine controller without change in both fault and fault-free cases. This is achieved by inserting an observer augmented by a proportional integral feedback to be capable to estimate different generator and rotor speed sensors fault for compensation purposes. Due to the dependency of the FTC strategy on the fault estimation the designed observer has the capability to estimate a wide range of time varying fault signal. Moreover, the robustness of the observer against the difference between the anemometer wind speed measurement and the unmeasurable effective wind speed signal has been taken into account. The corrected measurements fed to a T-S fuzzy dynamic output feedback controller (TSDOFC) designed to track the desired trajectory. The stability proof with H∞ performance and D-stability constraints is formulated as a Linear Matrix Inequality (LMI) problem. The strategy is illustrated using a non-linear wind turbine benchmark system model offered within a competition led by the companies Mathworks and KK-Electronic.