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Applications of data mining to time series of electrical disturbance data

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1 Author(s)
Cornforth, D. ; Commonwealth Sci. & Ind. Res. Organ. (CSIRO), Newcastle, NSW, Australia

Data mining is a term encompassing many methods. In this work unsupervised learning, or clustering, was applied to discover new insights from a public access database that lists major disturbances in the power network of the USA over the last 23 years. Results provide evidence that these disturbances can be placed into a few major groups, which can be characterized by region, cause and severity. This analysis also suggests a tendency for disturbances to occur more frequently in the early afternoon and in July. Statistical analysis confirms this conclusion. Such analysis provides a means to automatically characterize complex data, and may lead to fresh insights, and prove useful in planning and upgrade of infrastructure.

Published in:

Power & Energy Society General Meeting, 2009. PES '09. IEEE

Date of Conference:

26-30 July 2009