| 深度学习驱动的智能电网运行图像数据压缩技术 |
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| 引用本文： | 夏信,何传亮,吕英杰,王守志,张博,陈晨,陈海鹏,李美萱.深度学习驱动的智能电网运行图像数据压缩技术[J].红外与激光工程,2022,51(12):20220097-1-20220097-6. |
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| 作者姓名： | 夏信 何传亮 吕英杰 王守志 张博 陈晨 陈海鹏 李美萱 |
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| 作者单位： | 1.国家电网公司电力线通信应用技术实验室 北京智芯微电子科技有限公司，北京 102200 |
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| 基金项目： | 北京智芯微电子科技有限公司实验室开放基金项目 |
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| 摘 要： | 随着智能电网的快速发展，用于监视电网运行状况的测量设备大规模投入，其产生的海量运行图像等监视数据由于规模大、维度高、数据冗余等问题难以得到有效利用。为了进一步提高电力大数据的分析应用能力，文中提出一种基于深度学习的电网运行图像数据压缩方法，考虑电网图像监视数据在时序上的耦合关联，通过卷积神经网络对电网运行图像数据进行压缩，有效减少了电网运行图像数据的冗余度。与其他方法相比，基于卷积神经网络的图像数据压缩模型不依赖于人工的数据特征提取和工程经验，可以直接以电网中采集到的原始图像数据的灰度函数作为模型的输入，将数据的特征提取和分类合二为一，实现电网运行图像数据的高效、便捷压缩。通过仿真进行了文中所提方法有效性的验证，结果表明，与其他神经网络相比，所提方法在电网图像压缩效率及压缩精度中具有较强优势。
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| 关 键 词： | 智能电网 深度学习 数据驱动 卷积神经网络 图像压缩 |
| 收稿时间： | 2022-01-21 |
Image data compression technology of smart grid operation based on deep learning |
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| Abstract: | With the rapid development of smart grids, large-scale investment in measurement equipment for monitoring the operation of power grids, the monitoring data such as massive operation images generated by them is difficult to be effectively utilized due to problems such as large scale, high dimension and data redundancy. In order to further improve the analysis and application ability of power big data, this paper proposes a power grid operation image data compression method based on deep learning. Considering the coupling correlation of power grid image monitoring data in time series, the power grid operation image data is compressed through convolutional neural network, effectively reducing the redundancy of power grid operation image data. Compared with other methods, the image data compression model based on convolutional neural network does not rely on manual data feature extraction and engineering experience, and can directly use the grayscale function of the original image data collected from the power grid as the input of the model, and the data The feature extraction and classification are combined into one, to achieve efficient and convenient compression of power grid operation image data. The effectiveness of the method proposed in this paper is verified by simulation. The results show that the proposed method has strong advantages in power grid image compression efficiency and compression accuracy compared with other neural networks. |
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