基于堆稀疏自编码神经网络的电网安全预判
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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.
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.
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.
引文
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[2]薛禹胜,赖业宁.大能源思维与大数据思维的融合(一)大数据与电力大数据[J].电力系统自动化,2016,40(1):1-8.Xue Yusheng,Lai Yening.Integration of macro energy thinking and big data thinking part one applications and explorations[J].Automation of Electric Power Systems,2016,40(1):1-8.
[3]武同心,吕晓祥,王建全.Duhamel数值积分算法在电力系统暂态稳定分析中的应用[J].机电工程,2013,30(6):741-745.Wu Tongxin,LüXiaoxiang,Wang Jianquan.Application of duhamel integrals in power system transient stability analysis[J].Mechanical&Electrical Engineering Magazine,2013,30(6):741-745.
[4]刘怀东,张江红,刘沛龙,等.基于改进解析法的小范围动态安全域搜索方法[J].电力自动化设备,2012,32(2):29-33.Liu Huaidong,Zhang Jianghong,Liu Peilong,et al.Small-range search of DSR based on improved analytical method[J].Electric Power Automation Equipment,2012,32(2):29-33.
[5]戴远航,陈磊,张玮灵,等.基于多支持向量机综合的电力系统暂态稳定评估[J].中国电机工程学报,2016,36(5):1173-1180.Dai Yuanhang,Chen Lei,Zhang Weiling,et al.Power system transient stability assessment based on multi-support vector machines[J].Proceedings of the CSEE,2016,36(5):1173-1180.
[6]Ji L,Wu J,Zhou Y,et al.Using trajectory clusters to define the most relevant features for transient stability prediction based on machine learning method[J].Energies,2016,9(11):898.
[7]Wang S,Lu S,Zhou N,et al.Dynamic-feature extraction,attribution,and reconstruction(DEAR)method for power system model reduction[J].IEEE Transactions on Power Systems,2014,29(5):2049-2059.
[8]He Y,Mendis G J,Wei J.Real-time detection of false data injection attacks in smart grid:a deep learning-based intelligent mechanism[J].IEEE Transactions on Smart Grid,2017,8(5):2505-2516.
[9]Ma J,Zhang J,Xiao L,et al.Classification of power quality disturbances via deep learning[J].IETE Technical Review,2016:1-8.
[10]Jetcheva J G,Majidpour M,Chen W P.Neural network model ensembles for building-level electricity load forecasts[J].Energy&Buildings,2014(84):214-223.
[11]Kuznetsova E,Li Y F,Ruiz C,et al.Reinforcement learning for microgrid energy management[J].Energy,2013,59(59):133-146.
[12]Xu J,Xiang L,Liu Q,et al.Stacked sparse autoencoder(SSAE)for nuclei detection on breast cancer histopathology images[J].IEEE Transactions on Medical Imaging,2016,35(1):119.
[13]Othman E,Bazi Y,Alajlan N,et al.Using convolutional features and a sparse autoencoder for land-use scene classification[J].International Journal of Remote Sensing,2016,37(10):2149-2167.