摘要：In this paper, a new method for the detection and classification of all types of power quality
disturbances is presented. In addition to separating the disturbance signals, the proposed
method is able to determine the type of disturbances. Disturbance waveforms are generated
based on IEEE 1159 standard and they are de-noised using discrete wavelet transform. To
detect the sinusoidal signals from disturbance signals, new criteria have been proposed. By
introducing these new criteria, the classification algorithm is not active for non-disturbance
signals. Therefore, the computation time is reduced. If a signal has disturbance, to extract the
required information, it is analyzed using discrete wavelet transform. Using this information,
the appropriate feature vectors are introduced. Parallel neural networks structures are proposed
for the classification of disturbances. The inputs of these networks are the introduced feature
vectors. The proposed method is done for all power quality disturbances including DC offset,
flicker, interrupt, sag, swell, harmonic, notching, impulsive transient, oscillatory transient and
eight combinations of these including the harmonics with transient, harmonic with flicker,
harmonic with sag, harmonic with swell, sag with flicker, swell with flicker, transient with sag
and transient with swell. The performance of this algorithm is compared with a single neural
network structure. The results indicate using the parallel neural networks structure,
computational time is much reduced and the accuracy of classification of power quality
disturbances is significantly increased. Comparison the obtained results by the method with
other methods, represents very high performance of the proposed method with precision %99.53.
