Meteorological Disaster Prevention Method for Power Grid Based on Improved Stacked Denoising Auto-encoder Network

被引：0
|
作者：
Cong W. [1 ]
Hu L. [1 ]
Sun S. [2 ]
Han H. [2 ]
Sun M. [1 ]
Wang A. [2 ]
机构：
[1] Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education (Shandong University), Jinan
[2] State Grid Shandong Electric Power Company, Jinan
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2019年 / 43卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Deep learning; Disaster prevention and mitigation of power grid; Meteorological information; Power grid fault; Stacked denoising auto-encoder; Synthetic minority over-sampling technique;
D O I：
10.7500/AEPS20180302003
中图分类号：
学科分类号：
摘要：
The operation and maintenance data of power grid show that the main causes of the power grid fault have shifted from the level of manufacturing technology of electric equipment and the level of on-site operation and maintenance to natural weather factors such as thunder and lightning, mountain fire, gale and icy disaster. Disaster prevention and mitigation of power grid should also focus on meteorological disaster. Aiming at the characteristics and regularities of association between meteorological and power grid faults, a method of grid weather disaster mitigation based on improved stacked denoising auto-encoder (SDAE) network is proposed. Based on the meteorological historical data and grid operation and maintenance data, the synthetic minority over-sampling technique (SMOTE) is used to reduce the imbalance of the original data set. Auto-encoder network completes the extraction of meteorological information features and the establishment of the relationship meteorological information and grid faults through unsupervised self-learning and supervised fine-tuning, and improves the robustness of the network by incorporating sparse term restrictions and noise-enhanced coding. The case study shows that the proposed SMOTE-SDAE-based meteorological disaster mitigation method can establish the correlation mapping relationship between meteorological information and power grid fault accurately and completely, and can make accurate prediction for whether the given meteorological conditions will cause grid disaster accidents or not. © 2019 Automation of Electric Power Systems Press.
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页码：42 / 49
页数：7
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