Efficient Probabilistic Analysis of Offshore Wind Turbines Based On

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Efficient Probabilistic Analysis of Offshore Wind Turbines Based On Efficient probabilistic analysis of offshore wind turbines based on time-domain simulations Von der Fakultät für Bauingenieurwesen und Geodäsie der Gottfried Wilhelm Leibniz Universität Hannover zur Erlangung des Grades Doktor-Ingenieur - Dr.-Ing. - genehmigte Dissertation von Clemens Janek Hübler M. Sc. 2019 Hauptreferent: Prof. Dr.-Ing. habil. Raimund Rolfes, Leibniz Universität Hannover Korreferent: Prof. John Dalsgaard Sørensen, M.Sc., Lic.techn., B.Com. Aalborg University Tag der mündlichen Prüfung: 11. Januar 2019 Abstract Offshore wind energy plays an important role in the successful implementation of the energy transition. However, without subsidies, it is not yet sufficiently competitive compared to other renewables or conventional fossil fuels. This is why offshore wind turbines have to be structurally optimised with regard to economic efficiency. One possibility to significantly increase economic efficiency is to improve the reliability or at least to assess present reliability levels precisely. For an accurate reliability assessment during the design phase, probabilistic analyses based on time-domain simulations have to be conducted. In this thesis, a methodol- ogy for a comprehensive probabilistic design of offshore wind turbines with special focus on their substructures is developed and applied. All investigations are based on time-domain simulations. This leads to more accurate results compared to semi-analytical approaches that are commonly used for probabilistic modelling at the expense of higher computing times. In contrast to previous probabilistic analyses, considering only particular aspects of the probabilistic design, this work defines a comprehensive analysis that can be split up into the following seven aspects: deterministic load model, resistance model (failure modes), uncer- tainty of inputs, design of experiments, sensitivity analysis, long-term extrapolation/lifetime distribution, and economic effects. For five of these aspects, scientific innovations are realised, while state-of-the-art approaches are applied for the last two aspects. First, a method is developed in order to efficiently consider soil effects in offshore wind turbine simulations. This method is integrated in a state- of-the-art model for offshore wind turbines to enhance it by considering soil characteristics. At the same time, the number of degrees of freedom is kept constant. Second, the uncertainty of the most important inputs, i.e. environmental conditions, is determined. Theoretical statistical distributions of environmental conditions - like wind speeds - are derived using real offshore measurement data. State-of-the-art approaches are improved by using more sophisticated distributions. This enables a better agreement of theoretical and empirical distributions. Moreover, a variety of environmental conditions - having been neglected so far - is taken into account. Although there is some inherent uncertainty for every input, this uncertainty influences relevant outputs (e.g. reliability) only in some cases. Therefore, third, global sensitivity analyses are applied to determine significant inputs. All other inputs are set to deterministic values to reduce computing times. Especially for non-linear systems, the developed approach is more accurate than commonly used sensitivity analyses in the field of wind energy. Since - due to computational limitations - it is not possible to simulate the entire lifetime of an offshore wind turbine, long-term extrapolations have to be applied. These extrapolations increase the uncertainty of lifetime estimations. If probabilistic inputs are used, this effect is even intensified. Hence, several improved sampling techniques are de- veloped. They enable a significant reduction of the extrapolation-based lifetime uncertainty compared to classic approaches. Finally, the effect of variable, distributed lifetimes on the economic profitability of wind farm projects is investigated. In contrast to state-of-the-art investigations, economic aspects are not included in the engineering model, but independent economic and engineering models are combined. This enables high-quality results in both disciplines. Using the methodology for comprehensive probabilistic designs that is developed here, it is demonstrated that probabilistic analyses of wind turbines using time-domain simulations are possible. For this purpose, it is necessary to reduce computing times by applying adequate sensitivity analyses and extrapolation techniques. These approaches are developed in this thesis. Moreover, based on present findings, well-founded recommendations for efficient and realistic probabilistic simulations of offshore wind turbines are given. Finally, since the profitability of wind turbines depends significantly on the service life and lifetimes scatter substantially, deterministic approaches cannot be economically optimised. Hence, probabilistic analyses are valuable. Keywords: Offshore wind energy; probabilistic analysis; uncertainty; fatigue Kurzfassung Für die erfolgreiche Verwirklichung der Energiewende spielt die Offshore-Windenergie eine entscheidende Rolle. Allerdings ist sie ohne Subventionen noch immer kaum konkurrenz- fähig gegenüber anderen erneuerbaren Energien oder fossilen Energieträgern. Daher ist eine strukturdynamische Optimierung von Offshore-Windkraftanlagen hinsichtlich ihrer Wirtschaftlichkeit unumgänglich. Eine Möglichkeit, die Wirtschaftlichkeit signifikant zu steigern, ist die Verbesserung oder zumindest genaue Kenntnis der Zuverlässigkeit von Windkraftanlagen. Um Zuverlässigkeiten bereits in der Entwurfsphase präzise zu bestimmen, müssen probabilistische Analysen, die auf Simulationen im Zeitbereich basieren, durchgeführt werden. Die Entwicklung einer umfassenden Methodik für den probabilistischen Entwurf von Offshore-Windkraftanlagen mit dem Fokus auf der Substruktur und deren Anwendung ist der Kern dieser Arbeit. Hierbei ist insbesondere hervorzuheben, dass alle Untersuchungen auf Simulationen im Zeitbereich basieren. Dies erhöht im Vergleich zu den in der Probabilistik oft verwendeten semi-analytischen Ansätzen die Genauigkeit, wobei auch die Rechenzeiten ansteigen. Im Gegensatz zu bisherigen probabilistischen Ansätzen, die zumeist nur bestimmte Teilaspekte des probabilistischen Entwurfs berücksichtigen, wird hier eine umfassende probabilistische Analyse definiert, die aus den folgenden sieben Teilaspekte besteht: Deterministisches Lastmodell, Widerstandsmodellierung (Fehlermöglichkeiten), Unschärfe in Eingangsgrößen, numerische Versuchsplanung, Sensitivitätsanalyse, Langzeitextrapolation/Lebensdauervertei- lung und wirtschaftliche Aspekte. Wissenschaftliche Neuerungen werden bei fünf der zuvor genannten Punkten erzielt. Für die anderen beiden Teilaspekte werden Ansätze nach dem Stand der Forschung verwendet. Zunächst wird eine effiziente Methode zur Berücksichtigung von Bodeneigenschaften entwi- ckelt. Diese Methode wird in eine Offshore-Windkraftanlagenmodellierung nach dem Stand der Technik eingebunden, um diese hinsichtlich der Berücksichtigung von Bodeneigenschaften zu erweitern. Gleichzeitig wird die Anzahl der Freiheitsgrade nicht erhöht. Der zweite Schritt behandelt die Unschärfe der wichtigsten Eingangsgrößen, der Umgebungsbedingungen. Hier- zu werden reale Offshore-Messdaten verwendet, um theoretische Verteilungsfunktionen für Einwirkungsparameter wie Windgeschwindigkeiten abzuleiten. Im Vergleich zu bestehenden Ansätzen werden durch komplexere Verteilungsfunktionen verbesserte Übereinstimmungen mit den empirischen Verteilungen erzielt. Außerdem wird eine Vielzahl von bisher vernach- lässigten Umgebungsbedingungen untersucht. Obwohl jede Größe eine gewisse inhärente Unschärfe aufweist, hat die Unschärfe nur in einigen Fällen einen Einfluss auf die relevanten Ergebnisse (z. B. die Zuverlässigkeit). Daher beschäftigt sich der dritte Schritt mit der Entwicklung globaler Sensitivitätsverfahren zur Bestimmung jener Eingangsgrößen, deren Unschärfe relevant ist. Alle anderen Eingangsgrößen können deterministisch behandelt werden, um Rechenzeit zu sparen. Das entwickelte Verfahren zeichnet sich im Vergleich zu in der Windenergie üblichen Ansätzen durch eine hohe Genauigkeit in Bezug auf nichtlineares Systemverhalten aus. Für Windkraftanlagen ist die Langzeitextrapolation von besonderer Bedeutung, da die Simulation der gesamten Lebensdauer mit heutiger Rechenleistung nicht annähernd möglich ist. Diese Extrapolation stellt eine mögliche Fehlerquelle dar und erhöht die Unschärfe der Lebensdauerberechnung. Da diese Problematik durch die Verwendung von probabilistischen Eingangsgrößen verstärkt wird, werden im vierten Schritt verbesserte Stichprobenverfahren entwickelt. Diese Verfahren sind in der Lage die extrapolationsbedingte Unsicherheit der Lebensdauervorhersage im Vergleich zu klassischen Verfahren deutlich zu reduzieren. Im abschließenden Schritt wird der Einfluss einer nicht konstanten (verteilten) Lebensdauer auf die wirtschaftliche Profitabilität von Windparkprojekten untersucht. Im Gegensatz zu Untersuchungen nach dem Stand der Forschung werden hierfür nicht nur wirtschaftliche Aspekte im Ingenieurmodell berücksichtigt, sondern eigenständige ökono- mische und Ingenieurmodelle kombiniert. Dies ermöglicht qualitativ hochwertige Ergebnisse in beiden Bereichen. Mit Hilfe der hier entwickelten umfassenden Methodik kann gezeigt werden, dass probabilis- tische Analysen von Windkraftanlagen mit Simulationen im Zeitbereich realisierbar sind. Hierzu müssen Rechenzeiten durch den Einsatz geeigneter Sensitivitäts- und Extrapola- tionsverfahren
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