Structural health monitoring for wind turbine extended life operation
The first larger offshore wind farms approach the end of their design lifetime, which is typically 20 years. Extending the operation of aging wind farms is an appealing option to increase profit of wind energy projects. Continued operation is possible if the system holds a remaining useful lifetime beyond the original lifetime anticipated during design. Thus, the research focuses on development and implementation of remaining useful lifetime assessment methods and decision theories for lifetime extension.
Structural health monitoring as a general field of research has provided some cost effective approaches to assess the condition of turbine components. These methods benefit from improved mathematical models for the evolution of component wear and better assessment of site-specific loading. In the context of extended lifetime operation decision, further development of these methods is required for successful integration into design models.
A probabilistic fracture mechanics (FM) model is set up accounting for load, material and model uncertainties in the fatigue assessment of offshore wind support structures. This FM model is updated with information from inspections and monitoring using Bayesian analysis. Based on previous results, predictions of remaining useful lifetimes of offshore wind support structures are derived. The implemented model is verified with simulations and extended to support risk based decisions on continued operation taking repair and maintenance activities into account. Finally, a decision model is set up to evaluate the operational and financial risk of different operational scenarios like continued operation or uprating. In order to address the economic issues related to life extension for wind turbines, current wind turbine operations and maintenance strategies and costs have to be reviewed and OPEX forecasts based on different operational scenarios need to be implemented as part of a decision model.
Norwegian University of Science and Technology (NTNU)
Tentative schedule: January – July 2016
University of Strathclyde (USTRATH)
Tentative schedule: March – July 2017
Ziegler, L, Voormeeren, S, Schafhirt, S and Muskulus, M (2016). Design clustering of offshore wind turbines using probabilistic fatigue load estimation. Renewable Energy, 91, 425-433. DOI: http://dx.doi.org/10.1016/j.renene.2016.01.033 Link: http://www.sciencedirect.com/science/article/pii/S0960148116300337
Reder, M, Gonzalez, E and Melero, JJ (2016) Wind Turbine Failures – Tackling current Problems in Failure Data Analysis, Journal of Physics: Conference Series, 753(072027), DOI: 10.1088/1742-6596/753/7/072027. Full text: http://iopscience.iop.org/article/10.1088/1742-6596/753/7/072027
Ziegler, L and Muskulus, M (2016). Fatigue reassessment for lifetime extension of offshore wind monopile substructures, Journal of Physics: Conference Series, 753 (092010). IOP Publishing. DOI: 10.1088/1742-6596/753/9/092010 Full text: http://iopscience.iop.org/article/10.1088/1742-6596/753/9/092010
Ziegler, L, Schafhirt, S, Scheu, M, & Muskulus, M (2016). Effect of Load Sequence and Weather Seasonality on Fatigue Crack Growth for Monopile-based Offshore Wind Turbines. Energy Procedia, 94, 115-123. DOI: http://dx.doi.org/10.1016/j.egypro.2016.09.204 Link: http://www.sciencedirect.com/science/article/pii/S1876610216308736
Ziegler, L and Muskulus, M (2016). Lifetime extension of offshore wind monopiles: assessment process and relevance of fatigue crack inspection. 12th EAWE PhD Seminar on Wind Energy in Europe, Lyngby, Denmark. Download Full Text
Ziegler, L (2016). Lifetime extension of offshore wind monopiles. INORE 10th European Symposium, Nantes, France. Download Full Text
Ziegler, L, Lange, J, Smolka, U, & Muskulus, M (2016). Repowering and life extension: when does it make sense to switch? WindEurope Summit 2016, Hamburg, Germany. Link: https://windeurope.org/summit2016/conference/proceedings/index2.php
MSc Offshore Engineering and Dredging
MSc Technology-Wind Energy
Host Institution: RAMBOLL