Abstract
Abstract. Knowledge about the expected duration and intensity of wind power ramps is important when planning the integration of wind power production into an electricity network. The detection and classification of wind power ramps is not straightforward due to the large range of events that is observed and the stochastic nature of the wind. The development of an algorithm that can detect and classify wind power ramps is thus of some benefit to the wind energy community. In this study, we describe a relatively simple methodology using a wavelet transform to discriminate ramp events. We illustrate the utility of the methodology by studying distributions of ramp rates and their duration using 2 years of data from the Belgian offshore cluster. This brief study showed that there was a strong correlation between ramp rate and ramp duration, that a majority of ramp events were less than 15 h with a median duration of around 8 h, and that ramps with a duration of more than a day were rare. Also, we show how the methodology can be applied to a time series where installed capacity changes over time using Swedish onshore wind farm data. Finally, the performance of the methodology is compared with another ramp detection method and their sensitivities to parameter choice are contrasted.
Highlights
Rapid changes in wind speed can cause ramps in the wind power production of a wind farm
Low-level jets are known to be more prevalent during the evening hours at the location of some of the Belgian offshore wind farms (Kalverla et al, 2019) which may contribute to the increase in power generation observed during this period
We have presented a relatively simple methodology based on a wavelet transform and the use of surrogates to discriminate and extract ramp events
Summary
Rapid changes in wind speed can cause ramps in the wind power production of a wind farm. There is no accepted definition or classification of wind power ramps except that they are manifested in terms of a significant change in production over a relatively short time. An optimal method based on scoring functions (Sevlian and Rajagopal, 2013) was used to detect ramps of varying lengths at a US wind farm. These authors used a piecewise linear trending fit to remove short-term stochastic fluctuations. We build on this work by demonstrating how a wavelet transform can be used in conjunction with the generation of wind power surrogates to give a robust method for the detection of wind power ramps of varying magnitude and duration. We compare the methodology with another commonly used approach, namely the min–max method (Bianco et al, 2016)
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