基于堆稀疏自编码神经网络的电网安全预判

doi:10.13190/j.jbupt.2017-100

北京邮电大学学报 ›› 2017, Vol. 40 ›› Issue (6): 43-49.doi: 10.13190/j.jbupt.2017-100

基于堆稀疏自编码神经网络的电网安全预判

李晓宇¹, 李书芳¹, 李文启², 田世明³

1. 北京邮电大学网络体系构建与融合北京市重点实验室, 北京 100876;
2. 国网河南省电力公司, 郑州 50052;
3. 中国电力科学研究院, 北京 100192

收稿日期:2017-10-06 出版日期:2017-12-28 发布日期:2017-12-28
作者简介:李晓宇(1994-),男,硕士生;李书芳(1963-),女,教授,博士生导师,E-mail:lisf@bupt.edu.cn.
基金资助:
2016国家电网科技项目（SGTYHT/15-JS-191）

Prediction of Power System Security Based on Stacked Sparse Auto-Enconder Neural Network

LI Xiao-yu¹, LI Shu-fang¹, LI Wen-qi², TIAN Shi-ming³

1. Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2. State Grid Henan Electric Power Company, Zhengzhou 450052, China;
3. China Electric Power Research Institute, Beijing 100192, China

Received:2017-10-06 Online:2017-12-28 Published:2017-12-28
Supported by:

摘要/Abstract

摘要： 为了满足安全裕度概念下电力系统暂态稳定评估的准确率和时效性，提出了基于深度学习的堆稀疏自编码神经网络（SSAEN）预判模型.首先尝试将母线时序电压数据构成的矩阵看作蕴含暂态运行机制的模式图，然后利用SSAEN逐层级地挖掘模式图的主导特征，并通过分析SSAEN的连接权重阐述了主导特征及其演变过程.最后通过Logistic分类器识别主导特征，预判未来时刻系统的稳定性.基于IEEE39系统仿真样本进行了实验，实验结果表明，SSAEN具有较高的准确度和预测速度，可为系统暂态失稳情况提供足够的安全时间裕度.

关键词: 电力系统暂态稳定性, 堆稀疏自编码器, 主导特征, 分类器

Abstract: In order to satisfy the accuracy rate and timeliness performance for transition stability assessment(TSA) with a safety margin, a prediction model of stacked sparse auto-encoder network (SSAEN) is proposed based on deep learning. Firstly, the matrix obtained from the bus voltage can be treated as a pattern diagram with the TSA operating mechanism. Then, the dominant property of the pattern diagram hierarchically is mined by adopting SSAEN. And, the domination features and their evolution are described by analyzing the connection weights of the layers. Next, employing the logistic classifier can identify the dominant property. Finally, the system stability in the future time is successfully predicted. Competitive prediction speed and accuracy of the proposed SSAEN can be achieved from the simulation results in the IEEE-39-bus system, which is important to provide a sufficient safety margin when the system suffers from a temporary instability.

Key words: transient stability assessment, stacked sparse auto-encoder, dominant feature, classifier

中图分类号:

TM712

李晓宇, 李书芳, 李文启, 田世明. 基于堆稀疏自编码神经网络的电网安全预判[J]. 北京邮电大学学报, 2017, 40(6): 43-49.

LI Xiao-yu, LI Shu-fang, LI Wen-qi, TIAN Shi-ming. Prediction of Power System Security Based on Stacked Sparse Auto-Enconder Neural Network[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2017, 40(6): 43-49.

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基于堆稀疏自编码神经网络的电网安全预判

Prediction of Power System Security Based on Stacked Sparse Auto-Enconder Neural Network

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