Power quality event classification using complex wavelets phasor models and customized convolution neural network

Origin and triggers of power quality (PQ) events must be identified in prior, in order to take preventive steps to enhance power quality. However it is important to identify, localize and classify the PQ events to determine the causes and origins of PQ disturbances. In this paper a novel algorithm is presented to classify voltage variations into six different PQ events considering the space phasor model (SPM) diagrams, dual tree complex wavelet transforms (DTCWT) sub bands and the convolution neural network (CNN) model. The input voltage data is converted into SPM data, the SPM data is transformed using 2D DTCWT into low pass and high pass sub bands which are simultaneously processed by the 2D CNN model to perform classification of PQ events. In the proposed method CNN model based on Google Net is trained to perform classification of PQ events with default configuration as in deep neural network designer in MATLAB environment. The proposed algorithm achieve higher accuracy with reduced training time in classification of events than compared with reported PQ event classification methods.

THE ANALYSIS OF THE THREE-PHASE INDUCTION MOTOR WITH DEEP ROTOR BARS THROUGH NUMERICAL SIMULATIONS UTILIZING A MODIFIED MATHEMATICAL MODEL

The three-phase induction machine mathematical model presented in the paper, is adequate for applying to the deep rotor bars case. The rotor resistance R’r(r), respectively its leakage inductivity L’r(r), depend upon the rotor currents’ frequency fr because of the skin effect. Following the previous considerations, one developed slip dependent analytical expressions of the rotor circuit resistance R’r(s), respectively rotor circuit leakage reactance L’r (s). A modified space phasor based mathematical model of the deep bar induction motor is tested through simulations to assess the motor’s characteristics. The results are in accordance with the literature.