Fault diagnosis and sustainable control of wind turbines: Robust data-driven and model-based strategies

Fault Diagnosis and Sustainable Control of Wind Turbines: Robust Data-Driven and Model-Based Strategies discusses the development of reliable and robust fault diagnosis and fault-tolerant (‘sustainable’) control schemes by means of data-driven and model-based approaches. These strategies are able to cope with unknown nonlinear systems and noisy measurements. The book also discusses simpler solutions relying on data-driven and model-based methodologies, which are key when on-line implementations are considered for the proposed schemes. The book targets both professional engineers working in industry and researchers in academic and scientific institutions. In order to improve the safety, reliability and efficiency of wind turbine systems, thus avoiding expensive unplanned maintenance, the accommodation of faults in their early occurrence is fundamental. To highlight the potential of the proposed methods in real applications, hardware-in-the-loop test facilities (representing realistic wind turbine systems) are considered to analyze the digital implementation of the designed solutions. The achieved results show that the developed schemes are able to maintain the desired performances, thus validating their reliability and viability in real-time implementations. Different groups of readers-ranging from industrial engineers wishing to gain insight into the applications’ potential of new fault diagnosis and sustainable control methods, to the academic control community looking for new problems to tackle-will find much to learn from this work

Model Prediction-Based Approach to Fault Tolerant Control with Applications

Abstract— Fault-tolerant control (FTC) is an integral component in industrial processes as it enables the system to continue robust operation under some conditions. In this paper, an FTC scheme is proposed for interconnected systems within an integrated design framework to yield a timely monitoring and detection of fault and reconfiguring the controller according to those faults. The unscented Kalman filter (UKF)-based fault detection and diagnosis system is initially run on the main plant and parameter estimation is being done for the local faults. This critical information\ud is shared through information fusion to the main system where the whole system is being decentralized using the overlapping decomposition technique. Using this parameter estimates of decentralized subsystems, a model predictive control (MPC) adjusts its parameters according to the\ud fault scenarios thereby striving to maintain the stability of the system. Experimental results on interconnected continuous time stirred tank reactors (CSTR) with recycle and quadruple tank system indicate that the proposed method is capable to correctly identify various faults, and then controlling the system under some conditions

An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis

Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remaining useful life of systems. This paper presents a novel integrated framework for model-based distributed diagnosis and prognosis, where system decomposition is used to enable the diagnosis and prognosis tasks to be performed in a distributed way. We show how different submodels can be automatically constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs distributed fault diagnosis and prognosis in an efficient and robust manner

An Integrated Approach to Performance Monitoring and Fault Diagnosis of Nuclear Power Systems

In this dissertation an integrated framework of process performance monitoring and fault diagnosis was developed for nuclear power systems using robust data driven model based methods, which comprises thermal hydraulic simulation, data driven modeling, identification of model uncertainty, and robust residual generator design for fault detection and isolation. In the applications to nuclear power systems, on the one hand, historical data are often not able to characterize the relationships among process variables because operating setpoints may change and thermal fluid components such as steam generators and heat exchangers may experience degradation. On the other hand, first-principle models always have uncertainty and are often too complicated in terms of model structure to design residual generators for fault diagnosis. Therefore, a realistic fault diagnosis method needs to combine the strength of first principle models in modeling a wide range of anticipated operation conditions and the strength of data driven modeling in feature extraction. In the developed robust data driven model-based approach, the changes in operation conditions are simulated using the first principle models and the model uncertainty is extracted from plant operation data such that the fault effects on process variables can be decoupled from model uncertainty and normal operation changes. It was found that the developed robust fault diagnosis method was able to eliminate false alarms due to model uncertainty and deal with changes in operating conditions throughout the lifetime of nuclear power systems. Multiple methods of robust data driven model based fault diagnosis were developed in this dissertation. A complete procedure based on causal graph theory and data reconciliation method was developed to investigate the causal relationships and the quantitative sensitivities among variables so that sensor placement could be optimized for fault diagnosis in the design phase. Reconstruction based Principal Component Analysis (PCA) approach was applied to deal with both simple faults and complex faults for steady state diagnosis in the context of operation scheduling and maintenance management. A robust PCA model-based method was developed to distinguish the differences between fault effects and model uncertainties. In order to improve the sensitivity of fault detection, a hybrid PCA model based approach was developed to incorporate system knowledge into data driven modeling. Subspace identification was proposed to extract state space models from thermal hydraulic simulations and a robust dynamic residual generator design algorithm was developed for fault diagnosis for the purpose of fault tolerant control and extension to reactor startup and load following operation conditions. The developed robust dynamic residual generator design algorithm is unique in that explicit identification of model uncertainty is not necessary. Finally, it was demonstrated that the developed new methods for the IRIS Helical Coil Steam Generator (HCSG) system. A simulation model was first developed for this system. It was revealed through steady state simulation that the primary coolant temperature profile could be used to indicate the water inventory inside the HCSG tubes. The performance monitoring and fault diagnosis module was then developed to monitor sensor faults, flow distribution abnormality, and heat performance degradation for both steady state and dynamic operation conditions. This dissertation bridges the gap between the theoretical research on computational intelligence and the engineering design in performance monitoring and fault diagnosis for nuclear power systems. The new algorithms have the potential of being integrated into the Generation III and Generation IV nuclear reactor I&C design after they are tested on current nuclear power plants or Generation IV prototype reactors

Converter fault diagnosis and post-fault operation of a doubly-fed induction generator for a wind turbine

Wind energy has become one of the most important alternative energy resources because of the global warming crisis. Wind turbines are often erected off-shore because of favourable wind conditions, requiring lower towers than on-shore. The doubly-fed induction generator is one of the most widely used generators with wind turbines. In such a wind turbine the power converters are less robust than the generator and other mechanical parts. If any switch failure occurs in the converters, the wind turbine may be seriously damaged and have to stop. Therefore, converter health monitoring and fault diagnosis are important to improve system reliability. Moreover, to avoid shutting down the wind turbine, converter fault diagnosis may permit a change in control strategy and/or reconfigure the power converters to permit post-fault operation. This research focuses on switch fault diagnosis and post-fault operation for the converters of the doubly-fed induction generator. The effects of an open-switch fault and a short-circuit switch fault are analysed. Several existing open-switch fault diagnosis methods are examined but are found to be unsuitable for the doubly-fed induction generator. The causes of false alarms with these methods are investigated. A proposed diagnosis method, with false alarm suppression, has the fault detection capability equivalent to the best of the existing methods, but improves system reliability. After any open-switch fault is detected, reconfiguration to a four-switch topology is activated to avoid shutting down the system. Short-circuit switch faults are also investigated. Possible methods to deal with this fault are discussed and demonstrated in simulation. Operating the doubly-fed induction generator as a squirrel cage generator with aerodynamic power control of turbine blades is suggested if this fault occurs in the machine-side converter, while constant dc voltage control is suitable for a short-circuit switch fault in the grid-side converter.Wind energy has become one of the most important alternative energy resources because of the global warming crisis. Wind turbines are often erected off-shore because of favourable wind conditions, requiring lower towers than on-shore. The doubly-fed induction generator is one of the most widely used generators with wind turbines. In such a wind turbine the power converters are less robust than the generator and other mechanical parts. If any switch failure occurs in the converters, the wind turbine may be seriously damaged and have to stop. Therefore, converter health monitoring and fault diagnosis are important to improve system reliability. Moreover, to avoid shutting down the wind turbine, converter fault diagnosis may permit a change in control strategy and/or reconfigure the power converters to permit post-fault operation. This research focuses on switch fault diagnosis and post-fault operation for the converters of the doubly-fed induction generator. The effects of an open-switch fault and a short-circuit switch fault are analysed. Several existing open-switch fault diagnosis methods are examined but are found to be unsuitable for the doubly-fed induction generator. The causes of false alarms with these methods are investigated. A proposed diagnosis method, with false alarm suppression, has the fault detection capability equivalent to the best of the existing methods, but improves system reliability. After any open-switch fault is detected, reconfiguration to a four-switch topology is activated to avoid shutting down the system. Short-circuit switch faults are also investigated. Possible methods to deal with this fault are discussed and demonstrated in simulation. Operating the doubly-fed induction generator as a squirrel cage generator with aerodynamic power control of turbine blades is suggested if this fault occurs in the machine-side converter, while constant dc voltage control is suitable for a short-circuit switch fault in the grid-side converter

Application of a Combined Active Control and Fault Detection Scheme to an Active Composite Flexible Structure.

In this paper, the problem of increasing reliability of active control procedure is considered. Indeed, a design method of rejection perturbation in presence of potentially faults, on a flexible structure with integrated piezo-ceramics, is presented. The piezo-ceramics are used as actuators and sensors. A single unit based solution, which handles both control action and fault diagnosis is proposed. The algorithm uses H∞ optimization techniques. A full order model of the structure is first obtained via both finite-element (FE) approach and identification procedure. This model is then reduced in order to be used in our robust approach. By a suitable choice of weightings functions, the provided method is able to reject disturbance robustly and to estimate occurred faults. The case of sensors and actuators faults is discussed. The choice of weightings for diagnosis and control systems is also tackled. Finally, the effectiveness of this integrated method is confirmed by both simulation and experimental results

Autonomous power system intelligent diagnosis and control

The Autonomous Power System (APS) project at NASA Lewis Research Center is designed to demonstrate the abilities of integrated intelligent diagnosis, control, and scheduling techniques to space power distribution hardware. Knowledge-based software provides a robust method of control for highly complex space-based power systems that conventional methods do not allow. The project consists of three elements: the Autonomous Power Expert System (APEX) for fault diagnosis and control, the Autonomous Intelligent Power Scheduler (AIPS) to determine system configuration, and power hardware (Brassboard) to simulate a space based power system. The operation of the Autonomous Power System as a whole is described and the responsibilities of the three elements - APEX, AIPS, and Brassboard - are characterized. A discussion of the methodologies used in each element is provided. Future plans are discussed for the growth of the Autonomous Power System

Combining sensor monitoring and fault tolerant control to maintain flight control system functionalities

To maintain nominal flight control system functionalities during fault scenarios, enhancements of the state-of-practise angle of attack and airspeed sensor fault accommodation strategies are presented. The strategy combines a fault detection and diagnosis (FDD) system with a robust fault tolerant control law. The FDD system allows to maintain the nominal flight control law as long as at least one angle of attack and airspeed sensor are available. The FDD system is designed using advanced nullspace computation, optimization, and signal processing techniques. For the scenario of a total airspeed measurement loss, an airspeed independent longitudinal backup control law is designed using global optimization techniques. Using this law avoids the state-of-practise switch to a direct law in which the pilot must control the Elevator positions directly. The results from an extensive industrial validation and veri�cation campaign are reported

Fault Detection of Wind Turbine Induction Generators through Current Signals and Various Signal Processing Techniques

Producción CientíficaIn the wind industry (WI), a robust and effective maintenance system is essential. To minimize the maintenance cost, a large number of methodologies and mathematical models for predictive maintenance have been developed. Fault detection and diagnosis are carried out by processing and analyzing various types of signals, with the vibration signal predominating. In addition, most of the published proposals for wind turbine (WT) fault detection and diagnosis have used simulations and test benches. Based on previous work, this research report focuses on fault diagnosis, in this case using the electrical signal from an operating WT electric generator and applying various signal analysis and processing techniques to compare the effectiveness of each. The WT used for this research is 20 years old and works with a squirrel-cage induction generator (SCIG) which, according to the wind farm control systems, was fault-free. As a result, it has been possible to verify the feasibility of using the current signal to detect and diagnose faults through spectral analysis (SA) using a fast Fourier transform (FFT), periodogram, spectrogram, and scalogram

Fractional Order Fault Tolerant Control - A Survey

In this paper, a comprehensive review of recent advances and trends regarding Fractional Order Fault Tolerant Control (FOFTC) design is presented. This novel robust control approach has been emerging in the last decade and is still gathering great research efforts mainly because of its promising results and outcomes. The purpose of this study is to provide a useful overview for researchers interested in developing this interesting solution for plants that are subject to faults and disturbances with an obligation for a maintained performance level. Throughout the paper, the various works related to FOFTC in literature are categorized first by considering their research objective between fault detection with diagnosis and fault tolerance with accommodation, and second by considering the nature of the studied plants depending on whether they are modelized by integer order or fractional order models. One of the main drawbacks of these approaches lies in the increase in complexity associated with introducing the fractional operators, their approximation and especially during the stability analysis. A discussion on the main disadvantages and challenges that face this novel fractional order robust control research field is given in conjunction with motivations for its future development. This study provides a simulation example for the application of a FOFTC against actuator faults in a Boeing 747 civil transport aircraft is provided to illustrate the efficiency of such robust control strategies