Power quality disturbance classification method based on multimodal parallel feature extraction network

Zhanbei Tong; Yangxingyu Zhong; Gan Huang; Jiajun Tian; Wu Chen; Jianwei Zhong

doi:10.1117/12.3004907

9 October 2023 Power quality disturbance classification method based on multimodal parallel feature extraction network

Zhanbei Tong, Yangxingyu Zhong, Gan Huang, Jiajun Tian, Wu Chen, Jianwei Zhong

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127910L (2023) https://doi.org/10.1117/12.3004907
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

The problems of power quality disturbance (PQD) in modern power systems are complex and diverse, while the traditional model can only extract features of a single mode, which obtains insufficient information and affects the accurate identification of PQD. In order to obtain more comprehensive information and improve classification accuracy, this paper proposes a multimodal parallel feature extraction network for power quality disturbance classification. The model uses Gated Recurrent Unit (GRU) to extract temporal features. Meanwhile, the lightweight EfficientNet (LEfficientNet) module is designed to extract spatial features and fuse multimodal features. Then output to support vector machines (SVM) for classification. The classification results of 20 PQD simulated signals show that the proposed model achieves 99.89% correct classification rate, which is not only better than the performance of a single modal model, but also has lower training cost.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhanbei Tong, Yangxingyu Zhong, Gan Huang, Jiajun Tian, Wu Chen, and Jianwei Zhong "Power quality disturbance classification method based on multimodal parallel feature extraction network", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127910L (9 October 2023); https://doi.org/10.1117/12.3004907

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KEYWORDS

Feature extraction

Education and training

Machine learning

Feature fusion

Matrices

Systems modeling

Neural networks

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