Complex Power Quality Disturbance Identification Based on GAF-MTF Three-Channel Feature Fusion

The diversification of equipment in power system and the complexity of operating conditions gradually lead to the development of power quality disturbance to the complex power quality disturbances(CPQDs) which is released simultaneously by multiple types of disturbance. It is important to identify the type of CPQDs reliably and effectively for the stable operation of power grid. In this paper, a CPQDs identification architecture based on GASF-GADF-MTF three-channel multi-dimensional feature fusion plus transformer is proposed. Firstly, the CPQDs singal is converted into RGB three-channel data by using Gram summing field(GASF), Gram-diff-field(GADF) and Markov-transfer-field(MTF). The two-dime image of feature fusion is obtained. Then the transformer network is used to classify and discriminate the two-dime images containing the complex power quality disturbance characteristics. Simulation results show that the proposed method is reliable and effective, and has the advantages of good noise robustness and strong generalization ability.