Rough-Set-Based Feature Selection and Classification for Power Quality Sensing Device Employing Correlation Techniques

In this paper, we present a scheme of rough-set-based minimal set of feature selection and classification of power quality disturbances that can be implemented in a general-purpose microcontroller for embedded applications. The developed scheme can efficiently sense the power quality disturbances by the features extracted from the cross-correlogram of power quality disturbance waveforms. In this paper, a stand-alone module, employing microcontroller-based embedded system, is devised for efficiently sensing power quality disturbances in real time for in situ applications. The stand-alone module is developed on a PIC24F series microcontroller. Results show that the accuracy of the proposed scheme is comparable to that obtained in offline analysis using a computer. The method stated here is generic in nature and can be implemented for other microcontroller-based applications for topologically similar problems.