Robust Fault-Tolerant System Synthesis Via LMI

Chen, Z.; Patton, Ron J.; Chen, J.

doi:10.1016/s1474-6670(17)42424-4

Cited by 9 publications

(2 citation statements)

References 4 publications

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“…Alternatively, other methods could have been used such as [54], which preserves the nominal performance. Generally, these approaches rely on solving some optimisation problems where a controller is calculated subject to maximising the disturbance attenuation, using Linear Matrix Inequality (LMI) tools [14,55].…”

Section: Active Faut Tolerancementioning

confidence: 99%

Overview of Modelling and Advanced Control Strategies for Wind Turbine Systems

Simani

2015

Energies

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Abstract:The motivation for this paper comes from a real need to have an overview of the challenges of modelling and control for very demanding systems, such as wind turbine systems, which require reliability, availability, maintainability, and safety over power conversion efficiency. These issues have begun to stimulate research and development in the wide control community particularly for these installations that need a high degree of "sustainability". Note that this represents a key point for offshore wind turbines, since they are characterised by expensive and/or safety critical maintenance work. In this case, a clear conflict exists between ensuring a high degree of availability and reducing maintenance times, which affect the final energy cost. On the other hand, wind turbines have highly nonlinear dynamics, with a stochastic and uncontrollable driving force as input in the form of wind speed, thus representing an interesting challenge also from the modelling point of view. Suitable control methods can provide a sustainable optimisation of the energy conversion efficiency over wider than normally expected working conditions. Moreover, a proper mathematical description of the wind turbine system should be able to capture the complete behaviour of the process under monitoring, thus providing an important impact on the control design itself. In this way, the control scheme could guarantee prescribed performance, whilst also giving a degree of "tolerance" to possible deviation of characteristic properties or system parameters from standard conditions, if properly included in the wind turbine model itself. The most important developments in advanced controllers for wind turbines are also briefly referenced, and open problems in the areas of modelling of wind turbines are finally outlined.

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Section: Active Faut Tolerancementioning

confidence: 99%

Overview of Modelling and Advanced Control Strategies for Wind Turbine Systems

Simani

2015

Energies

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“…Simple observation will show if a feature is abnormal or not, the nature of the fault can then be determined, and the steps needed for maintenance and repair can be easily decided. Otherwise, the control system concept with fault detection and isolation (FDI) and fault-tolerant control (FTC) schemes have been already successfully applied to the fault detection of wind turbines [22][23][24][25][26][27][28][29][30][31][32]. It can be shown the more advantages of this kind schemes, especially in large wind power systems.…”

Section: Introductionmentioning

confidence: 99%

Blade Fault Diagnosis in Small Wind Power Systems Using MPPT with Optimized Control Parameters

Chen

Hung

2015

Energies

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Abstract:A systematic experiment verification of Chaos Embedded Sliding Mode Extremum Seeking Control for maximum power point tracking and a method for detecting possible faults in small wind turbine systems in advance are proposed in this paper. The chaotic logistic map is used to replace the random function in the particle swarm optimization algorithm for faster searching the optimal control parameter U . From the experimental results, it is verified that the Chaos Embedded Sliding Mode Extremum Seeking Control scheme has a better dynamic response than traditional Extremum Seeking Control scheme and Hill-Climbing Search scheme for maximum power point tracking. In the proposed scheme for fault detection, a chaotic synchronization method is used to transform the maximum power point tracking signal into a chaos synchronization error distribution diagram. It is then taken as the characteristic for fault diagnosis purposes. Finally, an extension theory pattern recognition technique is applied to diagnose the fault. Notably, the use of the chaotic dynamic errors as the fault diagnosis characteristic reduces the number of extracted features required, and therefore greatly reduces both the computation time and the hardware implementation cost. From the experimental results, it is shown that the fault diagnosis rate of the proposed method exceeds 98% not only in non-real-time but also in real-time of faults detection of the blades.

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Mathematical Modeling and Fault Description

Simani

2021

Diagnosis and Fault‐tolerant Control 1

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Robust Fault-Tolerant System Synthesis Via LMI

Cited by 9 publications

References 4 publications

Overview of Modelling and Advanced Control Strategies for Wind Turbine Systems

Overview of Modelling and Advanced Control Strategies for Wind Turbine Systems

Blade Fault Diagnosis in Small Wind Power Systems Using MPPT with Optimized Control Parameters

Mathematical Modeling and Fault Description

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