Detecting precursor patterns for frequency fluctuation in an electrical grid

Precursor pattern identification addresses the problem of detecting warning signals in data that herald an impending event of extraordinary interest. In the context of electrical power systems, identifying precursors to fluctuations in power generation in advance would enable engineers to put in place measures that mitigate against the effects of such fluctuations. In this research we use the Morlet wavelet to transform a time series defined on electrical power generation frequency which was sampled at intervals of 30 seconds to identify potential precursor patterns. The power spectrum that results is then used to select high coefficient regions that capture a large faction of the energy in the spectrum. We then subjected the high coefficient regions together with a contrasting low coefficient region to a non-parametric ANOVA test and our results indicate that one high coefficient region dominates by predicting an overwhelming percentage of the variation that occurs during the subsequent fluctuation event. These results suggest that the wavelet is an effective mechanism to identify precursor activity in electricity time series data.