Robust estimation and diagnosis of wind turbine pitch misalignments at a wind farm level

Wind turbine pitch misalignments provoke aerodynamic asymmetries which cause severe damage to the turbine. Hence, it is of interest to develop fault tolerant strategies to cope with pitch misalignments. Fault tolerant strategies require the information regarding the diagnosis and the estimation of the faults. However, most existing works focus only on open-loop misalignment diagnosis and do not provide robust fault estimates. In this work, we present a novel strategy to both estimate and diagnose pitch misalignments. The proposed strategy is developed at a wind farm level and it exploits altogether the information provided by the temporal and spatial relations of the turbines in the farm. Fault estimation is first addressed with a closed-loop switched observer. This observer is robust against disturbances and it adapts to the varying conditions along the wind turbine operation range. Fault diagnosis is then achieved via statistical-based decision mechanisms with adaptive thresholds. Both the observer and the decision mechanisms are designed to guarantee the desired performance. Introducing some restrictions over the number of simultaneous faulty turbines in the farm, the proposed approach is ameliorated via a bank of the aforementioned observers and decision mechanisms. Finally, the strategies are tested using a well-known wind farm benchmark

observer and energy-balance based approaches

Due to the increasing complexity of modern technical processes, the most critical issues in the design of an automated system nowadays are safety/reliability, higher performance and cost efficiency. Faults in process components can lead to a considerable reduce of the efficiency of the process, quality of the product and in some cases even result in fatalities. In order to avert these losses, an efficient diagnosis of the faults plays a central role. Therefore, fault diagnosis is becoming an essential part of modern control systems. Fault diagnosis of linear dynamical systems has been extensively studied since decades and well-established techniques exist in the literature. However, fault diagnosis for nonlinear dynamical systems is yet an active field of research. Since most of real systems are nonlinear in nature, classically, linear fault diagnosis techniques have been applied to nonlinear systems based on the linearized system model around an operating point. The drawback of this approach is the limited fault diagnosis performance. In order to fulfill the increasing demand of more effective fault diagnosis systems for nonlinear processes, a lot of attention has been paid to nonlinear fault diagnosis techniques, which is the major topic of this thesis. Different from linear systems, there is no uniform solution for the fault diagnosis of general nonlinear systems. Various schemes have been proposed for nonlinear systems with special structures. Among them, Lipschitz nonlinear systems have been intensively studied, since on one hand more general nonlinear systems can be transformed into Lipschitz nonlinear systems, and on the other hand, many linear fault diagnosis approaches can be extended to this kind of nonlinear systems. For Lipschitz nonlinear systems, observer-based fault detection approach has been mostly applied, which consists of an observer-based residual generator and a residual evaluator. Classically, residual generator and residual evaluator are designed separately. Since the performance of fault detection system is decided by residual generator and evaluator together, it can be expected that, higher fault detection performance can be achieved by designing these two units in an integrated manner instead of separate handling of them. Motivated by this fact, an integrated design approach of observer-based residual generator and evaluator is proposed for Lipschitz nonlinear systems. Besides the schemes extended from linear methods (i.e. observer-based approach, parity space approach etc.), new nonlinear fault diagnosis techniques have also been studied recently, which can be effectively applied to complex nonlinear systems i.e. switched nonlinear systems, hybrid nonlinear systems etc. Among them, new fault diagnosis schemes based on passivity and energy-balance which are closely related to system “energy” have a great potential due to their clear physical meanings. In this thesis, this approach is extended to a complete fault detection and isolation framework with the focus on passive nonlinear systems. The fault diagnosis methodologies proposed in this thesis are tested with the design examples in the respective chapters and with the robot manipulator benchmark problem. The simulation results show the effectiveness of the proposed schemes.Aufgrund der zunehmenden Komplexität moderner technischer Verfahren sind heutzutage Sicherheit/Zuverlässigkeit, höhere Leistung und Kosteneffizienz wichtige Probleme bei der Gestaltung eines automatisierten Systems. Fehler in Prozesskomponenten führen zu einer erheblichen Reduzierung im Wirkungsgrad des Prozesses, in der Qualität des Produktes und können im schlimmsten Fall sogar katastrophale Folgen haben. Um dies zu vermeiden ist eine effiziente Diagnose der Fehler von zentraler Bedeutung. Fehlerdiagnose ist daher ein wesentlicher Bestandteil von modernen Steuerungssystemen geworden. Die Fehlerdiagnose bei linearen dynamischen Systemen wurde seit Jahrzehnten ausführlich untersucht und gut etablierte Techniken existieren in der Literatur, dagegen ist die Fehlerdiagnose für nichtlineare dynamische Systeme noch ein aktives Forschungsfeld. Da die meisten realen Systemen nichtlineare sind, werden lineare Fehlerdiagnosetechniken meistens auf ein linearisiertes Systemmodell angewendet, was sich jedoch nachteilig auf die Leistung auswirkt. Deshalb gewinnt nichtlineare Fehlerdiagnosetechnik zur Erfüllung der wachsenden Nachfrage nach einer besseren Fehlerdiagnose für nichtlineare Prozesse immer mehr an Bedeutung und ist daher das Hauptthema dieser Dissertation. Da es keine einheitliche Lösung für die Fehlerdiagnose allgemeiner nichtlinearer Systeme gibt werden bestimmte nichtlineare Systeme mit speziellen Strukturen untersucht. Unter ihnen sind besonders die Lipschitz Systeme intensiv untersucht worden, da einerseits viele allgemeine nichtlineare Systeme in Lipschitz Systeme umgewandelt werden können und andererseits viele lineare Fehlerdiagnose Ansätze für diese Art von nichtlinearen Systemen erweitert werden können. Für Lipschitz Systeme werden meist beobachtergestützte Fehlerdetektionsverfahren verwendet, die aus einem Residuengenerator und einer Residuenauswertung bestehen. Klassischerweise werden Residuengenerator und Residuenauswertung getrennt entworfen. Da die Leistung der Fehlerdetektion sowohl von Residuengenerator als auch von Residuenauswertung gemeinsam abhängt, ist zu erwarten, dass eine höhere Fehlererkennungsleistung erreicht werden kann, wenn der Entwurf dieser beiden Einheiten integriert erfolgt. Deshalb wird hier ein integrierter Design-Ansatz zur beobachtergestützten Fehlererkennung für Lipschitz Systeme vorgeschlagen. Neben der Erweiterung von linearen Methoden (beobachtergestützter Ansatz, Paritäts Raum Ansatz usw.) werden neue, nichtlineare Fehlerdiagnosetechniken seit kurzem untersucht, die auch auf komplexe, nichtlineare Systeme (geschaltete nichtlineare Systeme, hybride nichtlineare Systeme usw.) angewendet werden können. Unter ihnen besonders Passivitäts- und Energie-Bilanz- gestützte Verfahren, die eng mit der " Systemenergien" verbunden sind, ein großes Potenzial durch ihre klare physikalische Bedeutung. Diese Verfahren werden in dieser Dissertation zu einer vollständigen Fehlererkennungs- und Isolationsmethodik mit dem Fokus auf passive nichtlineare Systeme erweitert. Die gezeigten Algorithmen werden in den entsprechenden Kapiteln anhand von numerischen Beispielen getestet. Weiterhin wird die Verwendung der Algorithmen an dem geläufigen Beispielprozess eines Roboter Manipulators gezeigt um deren Nutzen und Anwendbarkeit zu demonstrieren

Fault detection in nonlinear systems: an observer-based approach

An un-permitted deviation of at least one characteristic property or parameter of a system from standard condition is referred as a fault. Faults result in reduced efficiency of the system, reduced quality of the product, and sometimes complete breakdown of the process. This not only causes economic losses but may also result in fatalities. An early detection of faults can assist to avert these losses. Therefore, fault detection and process monitoring is becoming an essential part of modern control systems. Fault detection in linear dynamical systems has been extensively studied and well established techniques exist in the literature. However, fault detection for nonlinear dynamical systems is yet an active field of research. This work is motivated by the fact that most of real systems are nonlinear in nature and there is a need to develop fault detection techniques for nonlinear systems. Observer-based methods for fault detection have proven to be among the most capable approaches, therefore, this research is focused towards these methods. The first step in observer-based fault detection is to generate a symptom signal, called the residual signal, which carries the information of faults. This is done by comparing the measurements from the process to their estimates generated by an observer (filter). It is desired that the residual signal is sensitive to faults and robust against disturbances. This research presents new methods for designing observer (filter) to generate residual signal which is sensitive to faults and robust against disturbances. Three types of filters are proposed in this dissertation; these include a fault sensitive filter, disturbance attenuating filter, and a filter to achieve simultaneous attenuation of disturbances and amplification of faults. Despite the disturbance attenuation property of the proposed filters, the residual signal is not completely decoupled from the effect of disturbances and uncertainties. Therefore, a threshold is needed to care for the effect of disturbances and uncertainties. Selection of threshold plays an important role in the performance of the fault detection system. If it is selected too high, some faults will not be detected. Conversely, if it is selected too low, disturbances and uncertainties will result in false alarms. This research presents a new method to determine the threshold to avoid false-alarms and to minimize missed-detections. A threshold generator is proposed which is itself a dynamic system and produces a variable threshold. This threshold changes with the effects of uncertainties and disturbances and fits more tightly to the fault-free residual signal and, hence, the performance of fault detection system is improved. In addition to the residual generation stage, the efficiency of a fault detection system can also be optimized by post-filtering. A further contribution of this research is in proposing a post-filter which operates on the residual signal to generate a modified residual signal. This modified residual signal is simultaneously sensitive to faults and robust against disturbances. Together with this post-filter, a strategy is adopted to select a threshold which maximizes the fault detectability and minimizes the number of false-alarms