用视觉变压器识别电能质量扰动，准确率达98%以上
A Novel Approach to Classify Power Quality Signals Using Vision Transformers
- 将电能信号转为图像，用预训练视觉变压器分类
- 在17类扰动数据上达到98.28%精度和97.98%召回率
- 适用于智能电网安全监测，适合电力系统研究者
随着电子接口可再生能源与负载在智能电网中快速集成，电能质量扰动（PQD）分类成为提升电网安全性与效率的重要方向。本文提出一种基于视觉变压器（ViT）的新方法进行PQD分类。当发生扰动时，该方法首先将电能质量信号转换为图像，再利用预训练的ViT模型精确判断扰动类别。与以往仅限少数扰动类型或小规模数据集的研究不同，本方法在包含17类扰动的大规模数据集上进行训练与测试。实验结果表明，所提基于ViT的方法在相同数据集上实现98.28%的分类精度和97.98%的召回率，优于近期提出的多种技术。
原文摘要 · Abstract (English)
With the rapid integration of electronically interfaced renewable energy resources and loads into smart grids, there is increasing interest in power quality disturbances (PQD) classification to enhance the security and efficiency of these grids. This paper introduces a new approach to PQD classification based on the Vision Transformer (ViT) model. When a PQD occurs, the proposed approach first converts the power quality signal into an image and then utilizes a pre-trained ViT to accurately determine the class of the PQD. Unlike most previous works, which were limited to a few disturbance classes or small datasets, the proposed method is trained and tested on a large dataset with 17 disturbance classes. Our experimental results show that the proposed ViT-based approach achieves PQD classification precision and recall of 98.28% and 97.98%, respectively, outperforming recently proposed techniques applied to the same dataset.
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