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基于三维卷积神经网络的配电物联网异常辨识方法
作者:
作者单位:

1.强电磁工程与新技术国家重点实验室(华中科技大学),湖北省武汉市 430074;2.电力安全与高效湖北省重点实验室(华中科技大学电气与电子工程学院),湖北省武汉市 430074;3.国网河南省电力公司电力科学研究院,河南省郑州市 450052

摘要:

由于配电物联网中电力网与通信网高度耦合,单一网络的异常状态会交互作用至另一网络,可能进一步造成异常范围扩大,而单独采用电力网或通信网的异动信息难以全面、准确地辨识配电物联网异动源的类型和位置。因此,提出一种基于三维卷积神经网络(3D-CNN)的配电物联网异常类型辨识及定位方法。首先,分析了配电物联网通信流量特征并构建了基于Simulink和OPNET的配电物联网交互仿真模型;其次,提出了一种面向3D-CNN的样本构建方法,将配电物联网中每个节点的电气量和通信流量信息组成一个特征子像素,进而将配电物联网每个时刻的状态表示为一幅特征帧画面,形成隐含配电物联网异动过程的立方样本矩阵;随后,构建了包含三维特征提取网络和层级softmax分类器的深度学习模型,通过提取和辨识立方样本矩阵中隐含的异常信息,可以同时实现配电物联网异常类型和位置的判定;最后,利用IEEE 33节点配电物联网异常数据对模型进行测试,结果表明,所提方法可以对电力网短路故障、通信中断故障、通信数据异常引起的保护误动和拒动进行精确的分类及定位。

关键词:

基金项目:

国家电网有限公司总部科技项目(SGHADK00PJJS2000026)。

通信作者:

作者简介:

殷浩然(1997—),男,硕士研究生,主要研究方向:人工智能在电力系统中的应用。E-mail:hr_yin@126.com
苗世洪(1963—),男,通信作者,博士,教授,博士生导师,主要研究方向:电力系统保护与控制、配电网及微电网新技术、电网智能调度与自动化技术、压缩空气储能建模及应用等。E-mail:shmiao@hust.edu.cn
韩佶(1993—),男,博士研究生,主要研究方向:新能源技术与人工智能技术等。E-mail:han_ji1993@163.com


Anomaly Identification Method for Distribution Internet of Things Based on Three-dimensional Convolutional Neural Network
Author:
Affiliation:

1.State Key Laboratory of Advanced Electromagnetic Engineering and Technology (Huazhong University of Science and Technology), Wuhan 430074, China;2.Hubei Provincial Key Laboratory of Electric Power Security and High Efficiency (School of Electrical and Electronic Engineering, Huazhong University of Science and Technology), Wuhan 430074, China;3.Electric Power Research Institute of State Grid Henan Electric Power Company, Zhengzhou 450052, China

Abstract:

The power network and communication network are highly coupled in the distribution Internet of Things (DIoT). The anomalies of a single network interact with another network, which may lead to the expansion of anomaly range. However, it is difficult to comprehensively and accurately identify the types and locations of the anomaly source in the DIoT by using the information of the power network or the communication network alone. Therefore, this paper proposes an anomaly type identification and location method for DIoT based on the three-dimensional convolutional neural network (3D-CNN). Firstly, the communication flow characteristics of DIoT are analyzed and an interactive simulation model of DIoT based on Simulink and OPNET is built. Secondly, a 3D-CNN oriented sample construction method is proposed, in which the electric parameters and communication flow information of each node in DIoT are composed into a feature sub-pixel, and the state of DIoT at each moment is represented as a feature frame, thus forming a cubic sample matrix that contains the anomaly process of DIoT. Thirdly, a deep learning model is built, which includes the three-dimensional feature extraction network and the hierarchical softmax classifier. By extracting and identifying the abnormal information hidden in the cubic sample matrix, the type and location of anomalies in DIoT could be determined simultaneously. Finally, the model is tested by using abnormal data of the IEEE 33-node DIoT. The results show that the proposed method can precisely classify and locate the short-circuit fault, communication interruption fault, and protection maloperation and rejection caused by abnormal communication data.

Keywords:

Foundation:
This work is supported by State Grid Corporation of China (No. SGHADK00PJJS2000026).
引用本文
[1]殷浩然,苗世洪,韩佶,等.基于三维卷积神经网络的配电物联网异常辨识方法[J].电力系统自动化,2022,46(1):42-50. DOI:10.7500/AEPS20210629004.
YIN Haoran, MIAO Shihong, HAN Ji, et al. Anomaly Identification Method for Distribution Internet of Things Based on Three-dimensional Convolutional Neural Network[J]. Automation of Electric Power Systems, 2022, 46(1):42-50. DOI:10.7500/AEPS20210629004.
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  • 收稿日期:2021-06-29
  • 最后修改日期:2021-09-18
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  • 在线发布日期: 2022-01-05
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