Evaluation of pattern recognition algorithms for applications on power factor compensation

Moreira, Alexandre C.; Paredes, Helmo K. M.; de Souza, Wesley A.; Nardelli, Pedro H. J.; Marafão, Fernando P.; da Silva, Luiz C. P. (2017-12-05)
 
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https://doi.org/10.1007/s40313-017-0352-9

Moreira, Alexandre C.
Paredes, Helmo K. M.
de Souza, Wesley A.
Nardelli, Pedro H. J.
Marafão, Fernando P.
da Silva, Luiz C. P.
Springer Nature
05.12.2017

Moreira, A.C., Paredes, H.K.M., de Souza, W.A. et al. J Control Autom Electr Syst (2018) 29: 75. https://doi.org/10.1007/s40313-017-0352-9

https://rightsstatements.org/vocab/InC/1.0/
© Brazilian Society for Automatics--SBA 2017. This is a post-peer-review, pre-copyedit version of an article published in J Control Autom Electr Syst. The final authenticated version is available online at: http://dx.doi.org/10.1007/s40313-017-0352-9.
https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.1007/s40313-017-0352-9
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https://urn.fi/URN:NBN:fi-fe201803125832
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Abstract

This paper assesses different applied pattern recognition algorithms to decide the most appropriate power factor compensator for a particular point of common coupling. Power factor, current unbalance factor, total demand distortion, voltage harmonic distortion and reactive power daily variation, as well as human expertise, are the key parameters used to set each recognition algorithm. These algorithms are then trained with a series of both simulation and experimental data. Numerical results consistently indicate the decision-tree algorithm with depth 20 as the best classifier for power factor improvement in terms of all metrics considered in this work.

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