Wavelets Hybrid Neural Network for Harmonic Sources Recognition with the V-I Characteristics

This paper presents a wavelets hybrid neural network (WHNN) for harmonic source recognition with voltage-current (V-I) characteristics. With time-domain technique, WHNN is applied in the application of harmonic sources classification. The proposed hybrid network is a two-subnetwork architecture, consisting of self-organizing feature lattice network (SOFL) and wavelet layer connected in cascade. The first layer Morlet wavelets, as an extractor, are used to extract the features from voltages and currents, and SOFL is employed to classify the various feature patterns including electronic devices, DC/AC motor, and electric arc furnaces (EAFs) in second layer. The computational results have show that With the time-domain technique, the effectiveness of the proposed model is demonstrated by tests. The recognition results of different harmonic sources show the computational efficiency and accurate recognition.