电力系统自动化2019，Vol.43Issue(2)：42-49,8.DOI:10.7500/AEPS20180302003
基于改进深度降噪自编码网络的电网气象防灾方法
Meteorological Disaster Prevention Method for Power Grid Based on Improved Stacked Denoising Auto-encoder Network
摘要
Abstract
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.关键词
气象信息/电网防灾减灾/电网故障/合成少数类样本过采样技术/深度降噪自编码/深度学习Key words
meteorological information/disaster prevention and mitigation of power grid/power grid fault/synthetic minority over-sampling technique/stacked denoising auto-encoder/deep learning引用本文复制引用
丛伟,胡亮亮,孙世军,韩洪,孙梦晨,王安宁..基于改进深度降噪自编码网络的电网气象防灾方法[J].电力系统自动化,2019,43(2):42-49,8.基金项目
国家自然科学基金资助项目 (51377100).This work is supported by National Natural Science Foundation of China (No. 51377100). （51377100）
