A Methodology for the Time-scale-sensitive Evaluation of Wind Speed and Direction Variability

Significance statement

Wind speed is not only strongly variable: its variability is time-scale-dependent. The methodology introduced here is dedicated to an effective characterization of wind speed and direction as a function of time scale. It goes beyond the limits of the numerous classical methods that rely on wind speed values alone: while statistical distributions of the values in the time series are very useful, they only convey a part of the story; the temporal succession of those wind speed values can make a big difference from the point of view of the relation between recorded values and the actual power availability. The procedures used in this methodology rely on a multiscale analysis approach capable of capturing pattern persistence, quantifying the effect of the temporal succession of values in the time series as a function of time scale. The methodology is meant to support key aspects of site characterization, including power uncertainty evaluation and turbine yaw error minimization. In addition, it provides rich information for wind farm integration modeling.

Methodology Time-scale-sensitive Evaluation Wind Speed Direction Variability- renewable energy global innovations

Journal Reference

Energy Procedia, Volume 76, 2015, Pages 200–206.

Cristian Suteanu

Department of Geography & Environmental Studies / Department of Environmental Science, Saint Mary’s University, 923 Robie St., Halifax, Canada, B3H 3C3

Abstract

This paper introduces a methodology for the characterization of time-scale-dependent variability in wind patterns. Successive windows of wind speed time series are first analyzed using detrended fluctuation analysis. Isopersistence diagrams are then constructed to reflect the scale-by-scale variability of wind speed over time. Next, wind velocity vectors are projected on a plane that is rotated step by step, and a time-scale-sensitive analysis of the resulting projections is performed for each orientation of the plane, leading to an image of orientation–time-scale–persistence patterns. This methodology is designed to enhance the effectiveness of studies on site-dependent wind variability.

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About the author

Dr. Suteanu is an Associate Professor at Saint Mary’s University, Halifax, Canada, cross-appointed in the Department of Geography & Environmental Studies and the Department of Environmental Science. He is the Chairperson of the Environmental Science Department.

On one hand, he focuses on nonlinear analysis and modeling of complex systems, including the development of methodological approaches to environmental processes; applications include climate variability, renewable energy, and natural hazards. On the other hand, he studies epistemological aspects of our interactions with the environment. His courses include Environmental Pattern Analysis and Modeling, Environmental Information Management, as well as statistics, natural hazards, and graduate and post-doc courses on nonlinear approaches to natural complex systems.

 

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