Multiple harmonic-source classification using a Self-Organization Feature Map network with voltage-current wavelet transformation patterns

This paper proposes multiple harmonic-source classification using a Self-Organization Feature Map (SOFM) network with voltage (V)-current (I) wavelet transformation patterns. Using the V-I wavelet transformation (WT) patterns, a SOFM network is employed to separate non-harmonic loads from non-linear loads in a distribution system. Morlet wavelet functions are used as feature extractors to extract the features from voltage and current signals. These features are constructed from various V-I WT patterns, and can vary with different dilation and translation parameters. Therefore, the selectable features are relatively broad for real-time applications. Two-dimensional (2-D) patterns appear in different harmonic fluctuations with various harmonic orders, load levels, and power factors. A SOFM network is employed to classify the various V-I WT patterns, including non-linear electronic devices, AC/DC motors, and Electric Arc Furnaces (EAFs). By contrast with the traditional SOFM network and the support vector machine (SVM), the testing results show that the proposed method has a fast learning process, a high accuracy, and an adaptive capability with new add-in training patterns. It can be used as an added tool for power quality (PQ) engineers and can be integrated into monitor instruments.