Composite Harmonic Source Detection with Multi-Label Approach Using Advanced Fusion Method

Abstract

With the integration of clean energy and new power electronic devices into the power grid, the superposition of harmonic sources has become increasingly apparent and common. There is an urgent need to effectively identify composite harmonic sources in the new energy grid. This article proposes a multi-label composite harmonic source classification method that integrates knowledge representation with the transformer model. First, triplets from harmonic monitoring data are extracted and TransR models are used to train time-frequency feature representation vectors. Then, the transformer model is trained to learn the data features of different harmonic sources. Finally, based on the multi-label classification method, composite harmonic sources are identified. This article integrates the semantic information of time-frequency features into the data samples, increasing the interpretability of the model while expanding the inter-class features, which is conducive to the classification and recognition of the model. Compared with other deep learning recognition methods, verification based on simulation data and measured data shows that this method has low training complexity and higher recognition accuracy.