文章摘要
丛伟,胡亮亮,孙世军,等.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. DOI: 10.7500/AEPS20180302003.
CONG Wei,HU Liangliang,SUN Shijun, et al.Meteorological Disaster Prevention Method for Power Grid Based on Improved Stacked Denoising Auto-encoder Network[J].Automation of Electric Power Systems,2019,43(2):42-49. 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
DOI:10.7500/AEPS20180302003
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
KeyWords: meteorological information  disaster prevention and mitigation of power grid  power grid fault  synthetic minority over-sampling technique  stacked denoising auto-encoder  deep learning
上网日期:2018-11-19
基金项目:国家自然科学基金资助项目(51377100)
作者单位E-mail
丛伟 电网智能化调度与控制教育部重点实验室(山东大学), 山东省济南市 250061  
胡亮亮 电网智能化调度与控制教育部重点实验室(山东大学), 山东省济南市 250061 hlliang1995@163.com 
孙世军 国网山东省电力公司, 山东省济南市 250032  
韩洪 国网山东省电力公司, 山东省济南市 250032  
孙梦晨 电网智能化调度与控制教育部重点实验室(山东大学), 山东省济南市 250061  
王安宁 国网山东省电力公司, 山东省济南市 250032  
摘要:
      电网运维数据表明电网故障的主要原因已由电气设备制造工艺水平、现场运维水平等因素转向雷电、山火、大风、冰灾等自然气象因素,电网防灾减灾也应重点关注气象致灾。针对气象与电网故障之间的关联特点和规律,提出了一种基于改进深度降噪自编码(SDAE)网络的电网气象防灾方法。以气象历史数据和电网运维检修数据为基础,利用合成少数类样本过采样技术(SMOTE)降低原始数据集的不平衡度,自编码网络通过非监督自学习和有监督微调完成气象信息特征的提取和气象信息与电网故障映射关系的建立,并通过融入稀疏项限制和加噪编码来改善网络的鲁棒性。算例分析表明,所提出的基于SMOTE和SDAE的网络电网气象防灾方法,能够准确、全面地建立气象信息与电网故障之间的关联映射关系,能够对给定的气象条件是否会导致发生电网灾害事故进行准确的预判。
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.
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