K-Nearest Neighbor Classification of Harmonics Using Akaike Information Criterion
Abstract
If power quality can be maintained, high performance is possible for electrical devices. Harmonics is one of such problems that can cause performance to drop. Therefore, harmonics must be detected and prevented. This study aimed to be part of detection system designed to maintain power quality. Akaike Information Criterion is used to calculate features for power quality analysis. And for classification k-Nearest Neighbor classification is used. %94.8 accuracy is obtained from training set and %91.7 accuracy is obtained from test set. MATLAB is the program which is used for classification and feature calculation.
Keywords
Akaike Information,Harmonic Detection,k-Nearest Neighbor Classification,Power Quality
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