Robust fault estimation in wind turbine systems using GA optimisation

Odofin, Sarah; Sun, Kai

doi:10.1109/indin.2015.7281798

Cited by 9 publications

(8 citation statements)

References 10 publications

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“…In [75], FFT was used to spot the main frequency of disturbances, and evolutionary algorithm was employed to seek an optimal observer gain to minimize the effects in the estimation error from dominant disturbances as well as low-frequency faults so that a robust fault estimation algorithm was developed for a 5 MW wind turbine system. The work in [75] is a combination of signal-based and model-based methods.…”

Section: Hybrid Fault Diagnosis For Wind Turbine Systemsmentioning

confidence: 99%

An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems

2021

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Wind energy is contributing to more and more portions in the world energy market. However, one deterrent to even greater investment in wind energy is the considerable failure rate of turbines. In particular, large wind turbines are expensive, with less tolerance for system performance degradations, unscheduled system shut downs, and even system damages caused by various malfunctions or faults occurring in system components such as rotor blades, hydraulic systems, generator, electronic control units, electric systems, sensors, and so forth. As a result, there is a high demand to improve the operation reliability, availability, and productivity of wind turbine systems. It is thus paramount to detect and identify any kinds of abnormalities as early as possible, predict potential faults and the remaining useful life of the components, and implement resilient control and management for minimizing performance degradation and economic cost, and avoiding dangerous situations. During the last 20 years, interesting and intensive research results were reported on fault diagnosis, prognosis, and resilient control techniques for wind turbine systems. This paper aims to provide a state-of-the-art overview on the existing fault diagnosis, prognosis, and resilient control methods and techniques for wind turbine systems, with particular attention on the results reported during the last decade. Finally, an overlook on the future development of the fault diagnosis, prognosis, and resilient control techniques for wind turbine systems is presented.

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Section: Hybrid Fault Diagnosis For Wind Turbine Systemsmentioning

confidence: 99%

An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems

2021

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“…More precisely, we set: . Via the coordination transformation (11), the augmented system (10) The observability matrix is given by: An observer for this transformed system can be designed as follows:…”

Section: Design Of Parameter-varying Model-based Observermentioning

confidence: 99%

“…Fault diagnosis methods can be generally categorized into model-based approach, signal-based approach and data-driven approach [3][4][5][6]. Model-based fault diagnosis is one of the most powerful and popular system monitoring and fault diagnosis methods for wind turbine systems, and some results were reported in [7][8][9][10][11][12][13], generally utilizing linearized time-invariant models of wind turbine systems. However, wind turbines are nonlinear or parameter time-varying in nature.…”

Section: Introductionmentioning

confidence: 99%

Parameter-varying modelling and fault reconstruction for wind turbine systems

2018

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In this paper, parameter-varying technique is firstly addressed for modelling a 4.8MW 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 realtime 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.

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“…o Check the observer condition: Check whether (11) and (13) are satisfied. If yes, go to the next step;…”

Section: Design Procedures For Ga-based Fault Estimatormentioning

confidence: 99%

“…Robust techniques have been investigated and reviewed to reduce the impact of uncertainties in wind turbines by the unknown input-output (UIO) observer which has received much attention in the last decades [7]- [12]. In addition, the use of optimisation methods was discussed by [13]- [18], the sliding mode and the adaptive observer [19] suggested but needs more precision to reach its convergence estimate; proportional and integral observers and descriptor are known as the augmented observers [20]- [22], the high-gain estimator [23]. At present, another critical tool for the robustness design is the linear matrix inequality (LMI) technique [24]- [26].…”

Section: Introductionmentioning

confidence: 99%

Robust fault estimation for wind turbine energy via hybrid systems

2018

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The rapid development of modern wind turbine technology has led to increase demand for improving system reliability and practical concern for robust fault monitoring scheme. This paper presents the investigation of a 5MW Dynamic Wind Turbine Energy System that was designed to sustain condition monitoring and fault diagnosis with the goal of improving the reliability operations of universal practical control systems. A hybrid stochastic technique is proposed based on an augmented observer combined with eigenstructure assignment for the parameterisation and the genetic algorithm (GA) optimisation to address the attenuation of uncertainty mostly generated by disturbances. Scenarios-based are employed to explore sensor and actuator faults that have direct and indirect impacts on modern wind turbine system, based on monitoring components that are prone to malfunction. The analysis is aimed to determine the effect of concerned simulated faults from uncertainty in respect to environmental disturbances mostly challenged in real-world operations. The efficiency of the proposed approach will improve the reliability performance of wind turbine system states and diagnose uncertain faults simultaneously. The simulation outcomes illustrate the robustness of the dynamic turbine systems with a diagnostic performance to advance the practical solutions for improving reliable systems.

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Robust fault estimation in wind turbine systems using GA optimisation

Cited by 9 publications

References 10 publications

An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems

An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems

Parameter-varying modelling and fault reconstruction for wind turbine systems

Robust fault estimation for wind turbine energy via hybrid systems

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