Comparison Of Power Quality Disturbances Classification Based On Neural Network
Nway Nway Kyaw Win, Theingi Zin, Hla Myo Tun
Keywords: Power quality disturbances, wavelet transform, energy distribution, probabilistic neural network, multilayer feed forward neural network
Abstract: Power quality disturbances (PQDs) result serious problems in the reliability, safety and economy of power system network. In order to improve electric power quality events, the detection and classification of PQDs must be made type of transient fault. Software analysis of wavelet transform with multiresolution analysis (MRA) algorithm and feed forward neural network (probabilistic and multilayer feed forward neural network )based methodology for automatic classification of eight types of PQ signals (flicker, harmonics, sag, swell, impulse, fluctuation, notch and oscillatory) will be presented. The wavelet family, Db4 is chosen in this system to calculate the values of detailed energy distributions as input features for classification because it can perform well in detecting and localizing various types of PQ disturbances. This technique classifies the types of PQDs problem sevents.The classifiers classify and identify the disturbance type according to the energy distribution. The results show that the PNN can analyze different power disturbance types efficiently. Therefore, it can be seen that PNN has better classification accuracy than MLFF.
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