Analyzing harmonic monitoring data using data mining

Harmonic monitoring has become an important tool for harmonic management in distribution systems. A comprehensive harmonic monitoring program has been designed and implemented on a typical electrical MV distribution system in Australia. The monitoring program involved measurements of the three-phase harmonic currents and voltages from the residential, commercial and industrial load sectors. Data over a three year period has been downloaded and available for analysis. The large amount of acquired data makes it difficult to identify operational events that impact significantly on the harmonics generated on the system. More sophisticated analysis methods are required to automatically determine which part of the measurement data are of importance. Based on this information, a closer inspection of smaller data sets can then be carried out to determine the reasons for its detection. In this paper we classify the measurement data using data mining based on clustering techniques which can provide the engineers with a rapid, visually oriented method of evaluating the underlying operational information contained within the clusters. The paper shows how clustering can be used to identify interesting patterns of harmonic measurement data and how these relate to their associated operational issues.