Wind energy is one of the most promising possibilities for the decarbonization of the electricity grid. As wind turbines are usually located in remote areas and are often expensive to access, improving remote maintenance and diagnosis is crucial to future expansion of wind energy. Thus extensive efforts are made to facilitate the efficient off-site supervision of wind turbines using diverse data recorded from hundreds of sensors that monitor the current state of each unit. In the current iteration of these efforts, predictive maintenance techniques are used to model the normal behavior of multiple turbine components to automatically spot significant deviations from regular operation and notify diagnosticians. The goal of current development is to increase the level of automization to include diagnostic data from historical defects in order to accelerate diagnosis and actively learn from previous experience. To achieve this, challenges such as a high degree of heterogeneity, the rarity of defect events and the high diversity of defect types have to be overcome. In this talk, I will provide an overview of how EnBW employs machine learning techniques to detect anomalous behavior using its maintenance software. I will also discuss the challenges that arise and upcoming solutions to these issues, which will allow for a boost of both the economical and ecological efficiency of wind energy as we work towards a carbon-free future in the power sector.
Affiliation: scieneers GmbH
I discovered my passion for machine learning, data analysis, and statistics in the context of astroparticle physics, which provided me with a diverse portfolio of challenging problems along which to hone my problem-solving skills. However, it was clear to me relatively quickly that this passion goes far beyond the scope of physics. My main focus is always to come up with creative solutions with an eye on the state-of-the-art and to communicate the results in a clear, visually appealing and intuitive way. I get excited about new contexts very quickly and immersing myself in them gives me immense pleasure, especially when the fruits of my labor bring substantial and productive gains in knowledge.
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