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Monitoring of multivariate wind resources with self-organizing maps and slow feature analysis

Wind power is an important part of a sustainable and smart energy grid. Wind energy production datasets from hundreds of wind farms and thousands of windmills are collected, and have to be analyzed and understood. As wind is a volatile energy source, state observation has an important part to play for grid management, fault analysis and planning strategies of grid operators. We demonstrate how two approaches from unsupervised neural computation help to understand high-dimensional wind resource time series. The first approach for visualization of multivariate sequences is based on self-organizing feature maps. The output sequence allows the monitoring of the overall system state with a low-dimensional linear visualization that reflects the topological characteristics of the original wind data. We demonstrate the visualization on real-world wind resource measurements. The second approach shows how to identify the slowest feature in a multivariate wind time series, also known as driving force, with the help of slow feature analysis. Experiments, parameter analyses, and first interpretations demonstrate the capabilities of the approaches.