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

Abstract

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.