Abstract
To improve the precision of classification and recognition of transient power quality disturbances, a new algorithm based on spectral kurtosis (SK) and neural network is proposed. In the proposed algorithm, Morlet complex wavelet is used to obtain the WT-based SK of two kinds of disturbances, such as the impulse transient and oscillation transient. Two characteristic quantities, i.e., the maximum value of SK and the frequencies of the signals, are chosen as the input of neural network for the classification and recognition of transient power quality disturbances. Simulation results show that the transient disturbance characteristics can be effectively extracted by WT-based SK. With RBF neural network, the two kinds of transient disturbances can be effectively classified and recognized with the method in the paper.
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Zhang, Q., Liu, Z., Chen, G. (2012). The Recognition Study of Impulse and Oscillation Transient Based on Spectral Kurtosis and Neural Network. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_7
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DOI: https://doi.org/10.1007/978-3-642-31362-2_7
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