Power quality detection and classification using wavelet and support vector machine (2019)
This work presents the identification and classification of various disturbances that affect the quality of energy, seen as the quality of the voltage wave (harmonics, sag, swell and flicker). For this, the wavelet transform is used, which allows to have characteristic patterns as input signals of the support vector machine, these are evaluated in their different configurations, bi-class, minimum output coding, error correcting output and one versus all. For all of them, in the first instance they were trained with 200 samples, then the results were validated with 100 samples and finally the evaluation was made with 500 different samples, obtaining that the best result is presented with the minimum output coding configuration.