基于数据增强和深度残差网络的电力系统暂态稳定预测

doi:10.11930/j.issn.1004-9649.201912113

中国电力 ›› 2020, Vol. 53 ›› Issue (1): 22-31.DOI: 10.11930/j.issn.1004-9649.201912113

• 信息物理电力系统(CPPS)专栏 • 上一篇下一篇

基于数据增强和深度残差网络的电力系统暂态稳定预测

周艳真¹, 查显煜^2,3, 兰健¹, 郭庆来¹, 孙宏斌¹, 薛峰^2,3, 王胜明^2,3

1. 清华大学电机工程与应用电子技术系, 北京 100084;
2. 南瑞集团有限公司(国网电力科学研究院有限公司), 江苏南京 211106;
3. 智能电网保护和运行控制国家重点实验室, 江苏南京 211106

收稿日期:2019-12-19 发布日期:2020-01-15
通讯作者: 郭庆来(1979-),男,通信作者,博士,副教授,从事能量管理技术、电压稳定与电压控制、信息物理系统(CPS)、电动汽车相关研究,E-mail:guoqinglai@tsinghua.edu.cn
作者简介:周艳真(1990-),女,博士,助理研究员,从事电力系统分析与控制、人工智能方法在电力系统中的应用相关研究,E-mail:zhouyzh@126.com
基金资助:
国家电网公司科技项目（综合机器学习和安全稳定量化分析的在线安全稳定分析技术研究）

Transient Stability Prediction of Power Systems Based on Deep Residual Network and Data Augmentation

ZHOU Yanzhen¹, ZHA Xianyu^2,3, LAN Jian¹, GUO Qinglai¹, SUN Hongbin¹, XUE Feng^2,3, WANG Shengming^2,3

1. Department of Electrical Engineering, Tsinghua University, Beijing 100084, China;
2. NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China;
3. State Key Laboratory of Smart Grid Protection and Control, Nanjing 211106, China

Received:2019-12-19 Published:2020-01-15
Supported by:
This work is supported by the Science and Technology Program of State Grid Corporation of China (Research on Online Security and Stability Analysis Technology Integrating the Machine Learning and Quantitative Analysis Method)

摘要/Abstract

摘要： 针对传统数据驱动的电力系统暂态稳定分析方法中，较少考虑输入数据存在噪声和信息缺失后对预测模型性能的影响问题，提出一种基于数据增强和深度残差网络的暂态稳定预测方法。首先，考虑噪声和信息缺失情况，对原始训练数据进行扩充；然后，采用发电机受扰后动态数据作为输入特征；考虑到输入的高维时序数据具有图像的特点，利用图像处理中一种特殊的卷积神经网络—深度残差网络构建用于暂态稳定评估的深层模型。算例分析表明，所提出的方法能够提高模型的泛化能力，在含噪声以及部分发电机信息缺失情况下具有更好的鲁棒性。

关键词: 暂态稳定, 深度学习, 深度残差网络, 数据增强, 电力系统, 噪声, 信息缺失

Abstract: In traditional data-driven power system transient stability assessment methods, the impact of noise in the collected data and the information missing problems are rarely considered for the transient stability prediction. To deal with these problems, this paper presents a method for transient stability prediction based on data augmentation and deep residual network (ResNet). Firstly, the original training data is extended with consideration of the noise data and data-missing conditions. Then, the real-time data of the disturbed generator is used as input features. Considering the similarity between high-dimensional time series data and images, the deep residual network, which is an improved algorithm based on convolutional neural networks, is used to build transient stability assessment model. The case studies show that the proposed method can improve the generalization ability of the model, and has better robustness in dealing with noise data or data missing problems.

Key words: transient stability, deep learning, deep residual network, data augmentation, power system, noise, information missing

周艳真, 查显煜, 兰健, 郭庆来, 孙宏斌, 薛峰, 王胜明. 基于数据增强和深度残差网络的电力系统暂态稳定预测[J]. 中国电力, 2020, 53(1): 22-31.

ZHOU Yanzhen, ZHA Xianyu, LAN Jian, GUO Qinglai, SUN Hongbin, XUE Feng, WANG Shengming. Transient Stability Prediction of Power Systems Based on Deep Residual Network and Data Augmentation[J]. Electric Power, 2020, 53(1): 22-31.

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基于数据增强和深度残差网络的电力系统暂态稳定预测

Transient Stability Prediction of Power Systems Based on Deep Residual Network and Data Augmentation

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基于数据增强和深度残差网络的电力系统暂态稳定预测

Transient Stability Prediction of Power Systems Based on Deep Residual Network and Data Augmentation

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