Drone image recognition and intelligent power distribution network equipment fault detection based on the transformer model and transfer learning

IF 2.6 4区 工程技术 Q3 ENERGY & FUELS Frontiers in Energy Research Pub Date : 2024-08-29 DOI:10.3389/fenrg.2024.1364445
Jiayong Zhong, Yongtao Chen, Jin Gao, Xiaohong Lv
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Abstract

In today’s era of rapid technological advancement, the emergence of drone technology and intelligent power systems has brought tremendous convenience to society. However, the challenges associated with drone image recognition and intelligent grid device fault detection are becoming increasingly significant. In practical applications, the rapid and accurate identification of drone images and the timely detection of faults in intelligent grid devices are crucial for ensuring aviation safety and the stable operation of power systems. This article aims to integrate Transformer models, transfer learning, and generative adversarial networks to enhance the accuracy and efficiency of drone image recognition and intelligent grid device fault detection.In the methodology section, we first employ the Transformer model, a deep learning model based on self-attention mechanisms that has demonstrated excellent performance in handling image sequences, capturing complex spatial relationships in images. To address limited data issues, we introduce transfer learning, accelerating the learning process in the target domain by training the model on a source domain. To further enhance the model’s robustness and generalization capability, we incorporate generative adversarial networks to generate more representative training samples.In the experimental section, we validate our model using a large dataset of real drone images and intelligent grid device fault data. Our model shows significant improvements in metrics such as specificity, accuracy, recall, and F1-score. Specifically, in the experimental data, we observe a notable advantage of our model over traditional methods in both drone image recognition and intelligent grid device fault detection. Particularly in the detection of intelligent grid device faults, our model successfully captures subtle fault features, achieving an accuracy of over 90%, an improvement of more than 17% compared to traditional methods, and an outstanding F1-score of around 91%.In summary, this article achieves a significant improvement in the fields of drone image recognition and intelligent grid device fault detection by cleverly integrating Transformer models, transfer learning, and generative adversarial networks. Our approach not only holds broad theoretical application prospects but also receives robust support from experimental data, providing strong support for research and applications in related fields.
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基于变压器模型和迁移学习的无人机图像识别与智能配电网设备故障检测
在当今科技飞速发展的时代,无人机技术和智能电力系统的出现为社会带来了极大的便利。然而,无人机图像识别和智能电网设备故障检测所面临的挑战也越来越大。在实际应用中,快速准确地识别无人机图像和及时发现智能电网设备故障,对于确保航空安全和电力系统的稳定运行至关重要。本文旨在整合变压器模型、迁移学习和生成式对抗网络,以提高无人机图像识别和智能电网设备故障检测的准确性和效率。在方法论部分,我们首先采用变压器模型,这是一种基于自我注意机制的深度学习模型,在处理图像序列、捕捉图像中复杂的空间关系方面表现出色。为了解决数据有限的问题,我们引入了迁移学习,通过在源域上训练模型来加速目标域的学习过程。为了进一步增强模型的鲁棒性和泛化能力,我们结合了生成式对抗网络,以生成更具代表性的训练样本。在实验部分,我们使用大量真实无人机图像数据集和智能电网设备故障数据验证了我们的模型。我们的模型在特异性、准确性、召回率和 F1 分数等指标上都有明显改善。具体来说,在实验数据中,我们观察到我们的模型在无人机图像识别和智能电网设备故障检测方面都比传统方法有明显优势。特别是在智能电网设备故障检测中,我们的模型成功捕捉到了细微的故障特征,准确率达到 90% 以上,与传统方法相比提高了 17% 以上,F1 分数达到 91% 左右,表现突出。总之,本文通过巧妙地整合 Transformer 模型、迁移学习和生成式对抗网络,在无人机图像识别和智能电网设备故障检测领域取得了显著的进步。我们的方法不仅具有广阔的理论应用前景,而且得到了实验数据的有力支持,为相关领域的研究和应用提供了有力支撑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Frontiers in Energy Research
Frontiers in Energy Research Economics, Econometrics and Finance-Economics and Econometrics
CiteScore
3.90
自引率
11.80%
发文量
1727
审稿时长
12 weeks
期刊介绍: Frontiers in Energy Research makes use of the unique Frontiers platform for open-access publishing and research networking for scientists, which provides an equal opportunity to seek, share and create knowledge. The mission of Frontiers is to place publishing back in the hands of working scientists and to promote an interactive, fair, and efficient review process. Articles are peer-reviewed according to the Frontiers review guidelines, which evaluate manuscripts on objective editorial criteria
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