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Harmonic monitoring has become an important tool for harmonic management in distribution system. A comprehensive harmonic monitoring program has been designed and implemented on a typical electrical medium-voltage 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...
In many of clustering algorithms, such as K-means and Fuzzy C-mean, the value of the expected numbers of clusters is often needed in advance as an input parameter to the algorithm. Other clustering algorithms estimate this number as the clustering process progresses using various heuristic techniques; however such techniques can also lead to a local minima within the solution space without finding...
Clustering is an important technique in data mining and machine learning in which underlying and meaningful groups of data are discovered. One of the paramount issues in clustering process is to discover the natural groups in the data set. A method based on the minimum message length (MML) has been developed to determine the optimum number of clusters (or mixture model size) in a power quality data...
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