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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The authors gratefully acknowledge the contributions of São Paulo Research Foundation (FAPESP) under Grant 2016/08645-9 and by Finnish Academy and CNPq/Brazil (n.490235/2012-3) as part of the joint project SUSTAIN, by Strategic Research Council/Aka BC-DC project (n.292854) for their financial support toward the development of this research.
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Moreira, A.C., Paredes, H.K.M., de Souza, W.A. et al. Evaluation of Pattern Recognition Algorithms for Applications on Power Factor Compensation. J Control Autom Electr Syst 29, 75–90 (2018). https://doi.org/10.1007/s40313-017-0352-9
- Active compensators
- Passive compensators
- Pattern recognition
- Power factor
- Reactive and harmonic compensation