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深度学习在智能电网中的应用现状分析与展望
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输配电装备及系统安全与新技术国家重点实验室(重庆大学), 重庆市 400044

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基金项目:

国家自然科学基金资助项目(51577018);重庆市科技计划项目基础科学与前沿技术研究专项重点项目(cstc2015jcyjBX0033)


Analysis and Prospect of Deep Learning Application in Smart Grid
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State Key Laboratory of Power Transmission Equipment & System Security and New Technology (Chongqing University, Chongqing 400044, China

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    摘要:

    深度学习是机器学习研究中的一个新领域,其强大的数据分析、预测、分类能力契合智能电网中大数据应用的需求。文中首先总结了深度学习基本思想,介绍深度学习的5种模型(生成式对抗网络、递归神经网络、卷积神经网络、堆叠自动编码器和深度信念网络)的结构、基本原理、训练方法,概括其应用特征。综述了电力系统中的故障诊断、暂态稳定性分析、负荷及新能源功率预测、运行调控等应用深度学习技术的研究现状。针对深度学习的技术特点,结合电力系统各生产环节,构建深度学习技术在电力系统中的应用框架。最后,从多能源系统运行调控、电力电子化系统安全分析、柔性设备故障诊断、电力信息物理系统的安全防护等方面对深度学习应用进行展望。

    Abstract:

    Deep learning is a new field of machine learning. Its powerful data analysis, prediction, and classification capabilities satisfy the needs of big data applications in smart grid. Firstly, this paper summarizes the basic ideas of deep learning, introduces the structures, basic principles and training methods of five models of deep learning(generative adversarial network, recurrent neural network, convolution neural network, stacked auto encoder and deep belief network), and summarizes their application characteristics. The applications of deep learning techniques in fault diagnosis, transient stability analysis, load and new energy power forecasting, operation control in power system are summarized. Based on the technical characteristics of deep learning and the production links of power system, the application framework of deep learning technology in power system is constructed. Finally, the application of deep learning is prospected in the aspects of multi-energy system operation regulation, power electronic system security analysis, flexible equipment fault diagnosis, and cyber-physical power system security protection.

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引用本文

周念成,廖建权,王强钢,等.深度学习在智能电网中的应用现状分析与展望[J].电力系统自动化,2019,43(4):180-191. DOI:10.7500/AEPS20180323002.
ZHOU Niancheng, LIAO Jianquan, WANG Qianggang,et al.Analysis and Prospect of Deep Learning Application in Smart Grid[J].Automation of Electric Power Systems,2019,43(4):180-191. DOI:10.7500/AEPS20180323002.

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  • 收稿日期:2018-03-23
  • 最后修改日期:2018-12-04
  • 录用日期:2018-09-21
  • 在线发布日期: 2018-11-30
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