Evaluation of automatic power quality classification in microgrids operating in islanded mode

Abstract

Microgrids operating in islanded mode are more prone to fundamental frequency variations and power quality distortions. To mitigate power quality problems, it is essential to first identify the type of distortion using a proper classifier. There are many classifiers in the literature. However, they are tested assuming that there is no fundamental frequency variation. In this paper, we in the effect of fundamental frequency variations on classification accuracy. For that purpose, a well-known classifier is tested with data sets of different fundamental frequencies. Then, accuracies are compared using statistical tests. To the best of our knowledge, this is the first work adopting this approach. The results of the comparison show that changes in fundamental frequency greatly affect classification accuracy. A large decrease occurs even with moderate frequency deviations. Therefore, future studies should consider this effect, since non-adapted classifiers may perform poorly in weak microgrids operating in islanded mode.