Abstract
This paper presents a methodology for the classification of disorders related to the area of Power Quality. Therefore, we used the Random Forest algorithm, which corresponds to an effective data mining technique, especially when dealing with large amounts of data. This algorithm uses a set of classifiers based on decision trees. In this sense, the performance of the proposed methodology was evaluated in a comparative way between the Random Forest and the type J48 Decision Tree. For this analysis to be possible, synthetic electrical signals were generated, where these disturbances were modeled through parametric equations. After the performance analysis, it was observed that the results were promising, since the Random Forest algorithm provides the best performance.
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Acknowledgement
The authors gratefully acknowledge the financial support for the development of this research provided by FAPESP (Process 2011/17610-0 and 2013/16778-0).
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Borges, F., Silva, I., Fernandes, R., Moraes, L. (2016). Classification of Power Quality Disturbances Using Forest Algorithm. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2016. Lecture Notes in Computer Science(), vol 9714. Springer, Cham. https://doi.org/10.1007/978-3-319-40973-3_24
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DOI: https://doi.org/10.1007/978-3-319-40973-3_24
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