基于改进卷积神经网络拓扑特征挖掘的配电网结构坚强性评估方法
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摘要
提出一种基于拓扑特征分析和深度卷积神经网络的配电网网架结构坚强性评估方法。将考虑分布式电源出力不确定性的配电网运行状态与配电网拓扑特性相结合,构建涵盖可靠性、运行裕度和结构鲁棒性3个配电网结构坚强性要点的拓扑指标集,提升评估指标刻画配电网实际状态的能力;通过多通道融合和多级池化改进卷积神经网络,解决了传统方法无法自主挖掘评估指标数据特征,以及难以直接分析不同维度的评价指标和同一指标不同尺寸的数据这两方面问题。通过对华东地区某中压配电网进行算例分析,说明所提出的评估方法的有效性和优越性。
A distribution network structure strength assessment method based on topology feature analysis and deep convolution neural network was proposed. Considering both uncertainties of distributed generation output in network operation and network topological features, a topology feature index set assessing network structure strength from the perspectives of reliability, security and structural robustness was built to raise the accuracy in describing distribution network operation states. The traditional convolution neural network was improved by combining multi-convolution channel merging and multi-scale pooling. The proposed method was capable of autonomously mining concealed topological characteristics and analyzing indices of different dimensions and index data of different scales, which were hardly achieved by traditional approaches. A case study of a medium-voltage distribution network in Eastern China was used to verify the validity and superiority of the proposed topology feature indices and the structure strength assessment model.
A distribution network structure strength assessment method based on topology feature analysis and deep convolution neural network was proposed. Considering both uncertainties of distributed generation output in network operation and network topological features, a topology feature index set assessing network structure strength from the perspectives of reliability, security and structural robustness was built to raise the accuracy in describing distribution network operation states. The traditional convolution neural network was improved by combining multi-convolution channel merging and multi-scale pooling. The proposed method was capable of autonomously mining concealed topological characteristics and analyzing indices of different dimensions and index data of different scales, which were hardly achieved by traditional approaches. A case study of a medium-voltage distribution network in Eastern China was used to verify the validity and superiority of the proposed topology feature indices and the structure strength assessment model.
引文
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[13]魏东,龚庆武,来文青,等.基于卷积神经网络的输电线路区内外故障判断及故障选相方法研究[J].中国电机工程学报,2016,36(S1):21-28.Wei Dong,Gong Qingwu,Lai Wenqing,et al.Research on internal and external fault diagnosis and fault-selection of transmission line based on convolutional neural network[J].Proceedings of the CSEE,2016,36(S1):21-28(in Chinese).
[14]刘威,张东霞,王新迎,等.基于深度强化学习的电网紧急控制策略研究[J].中国电机工程学报,2018,38(1):109-119.Liu Wei,Zhang Dongxia,Wang Xinying,et al.A decision making strategy for generating unit tripping under emergency circumstances based on deep reinforcement learning[J].Proceedings of the CSEE,2018,38(1):109-119(in Chinese).
[15]胡丽娟,刘科研,盛万兴,等.含随机出力分布式电源的配电网静态电压稳定快速概率评估方法[J].电网技术,2014,38(10):2766-2771.Hu Lijuan,Liu Keyan,Sheng Wanxing,et al.Fast probabilistic evaluation of static voltage stability in active distribution network considering random output from distributed generations[J].Power System Technology,2014,38(10):2766-2771(in Chinese).
[16]张李盈,范明天.配电网综合规划模型与算法的研究[J].中国电机工程学报,2004,24(6):59-64.Zhang Liying,Fan Mingtian.A new model and methodology for distribution network integration planning[J].Proceedings of the CSEE,2004,24(6):59-64(in Chinese).
[17]马志远,刘锋,沈沉,等.基于PageRank改进算法的电网脆弱线路快速辨识(一):理论基础[J].中国电机工程学报,2016,36(23):6363-6370.Ma Zhiyuan,Liu Feng,Shen Chen,et al.Rapid identification of vulnerable lines in power grid using modified PageRank algorithm(Part I):theoretical foundation[J].Proceedings of the CSEE,2016,36(23):6363-6370(in Chinese).
[18]马志远,刘锋,沈沉,等.基于PageRank改进算法的电网脆弱线路快速辨识(二):影响因素分析[J].中国电机工程学报,2017,37(1):36-44.Ma Zhiyuan,Liu Feng,Shen Chen,et al.Rapid identification of vulnerable lines in power grid using modified PageRank algorithm(Part II):factors affecting identification results[J].Proceedings of the CSEE,2017,37(1):36-44(in Chinese).
[19]Li Canbing,Liu Wencan,Cao Yijia,et al.Method for evaluating the importance of power grid nodes based on PageRank algorithm[J].IET Generation,Transmission&Distribution,2014,8(11):1843-1847.
[20]Krizhevsky A,Sutskever I,Hinton G E.ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems.Lake Tahoe,Nevada:ACM,2012:1097-1105.
[21]Billinton R,Wangdee W.Delivery point reliability indices of a bulk electric system using sequential Monte Carlo simulation[J].IEEE Transactions on Power Delivery,2006,21(1):345-352.
[22]Ioffe S,Szegedy C.Batch normalization:accelerating deep network training by reducing internal covariate shift[C]//Proceedings of the 32nd International Conference on Machine Learning.Lille,France:JMLR,2015.
[23]Lin Min,Chen Qiang,Yan Shuicheng.Network in network[J].arXiv preprint arXiv:1312.4400,2013.
[24]He Kaiming,Zhang Xiangyu,Ren Shaoqing,et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[C]//Proceedings of the 13th European Conference on Computer Vision.Zurich,Switzerland:Springer,2014.
[25]刘慧.基于KNN的中文文本分类算法研究[D].成都:西南交通大学,2010.Liu Hui.Research of Chinese text classification based on KNN[D].Chengdu:Southwest Jiaotong University,2010(in Chinese).
[26]王贺,胡志坚,张翌晖,等.基于聚类经验模态分解和最小二乘支持向量机的短期风速组合预测[J].电工技术学报,2014,29(4):237-245.Wang He,Hu Zhijian,Zhang Yihui,et al.A hybrid model for short-term wind speed forecasting based on ensemble empirical mode decomposition and least squares support vector machines[J].Transactions of China Electrotechnical Society,2014,29(4):237-245(in Chinese).
[27]Pan Zhijun,Zhang Yan.A novel centralized charging station planning strategy considering urban power network structure strength[J].Electric Power Systems Research,2016,136:100-109.
[2]杨文辉,毕天姝,黄少锋,等.基于电网生存性评估的关键线路识别方法[J].中国电机工程学报,2011,31(7):29-35.Yang Wenhui,Bi Tianshu,Huang Shaofeng,et al.An approach for critical lines identification based on the survivability of power grid[J].Proceedings of the CSEE,2011,31(7):29-35(in Chinese).
[3]杨丽徙,曾新梅,方强华,等.基于抗毁性分析的中压配电网络拓扑结构[J].电力系统自动化,2013,37(8):65-69.Yang Lixi,Zeng Xinmei,Fang Qianghua,et al.Medium voltage distribution network topology based on survivability analysis[J].Automation of Electric Power Systems,2013,37(8):65-69(in Chinese).
[4]白加林,刘天琪,曹国云,等.电力系统脆弱性评估方法综述[J].电网技术,2008,32(S2):26-30.Bai Jialin,Liu Tianqi,Cao Guoyun,et al.A survey on vulnerability assessment method for power system[J].Power System Technology,2008,32(S2):26-30(in Chinese).
[5]何秋泠.配电网网架结构的坚强性评估[D].成都:西南交通大学,2014.He Qiuling.The robustness degree assessment method of distribution grid structure[D].Chengdu:Southwest Jiaotong University,2014(in Chinese).
[6]杜瑞明.配电网运行过程坚强度评估研究[D].成都:西南交通大学,2015.Du Ruiming.The robustness degree evaluation of distribution grid operation process[D].Chengdu:Southwest Jiaotong University,2015(in Chinese).
[7]李宏达.配电网网架坚强度评估研究[D].成都:西南交通大学,2013.Li Hongda.The robustness degree evaluation of distribution grid structure[D].Chengdu:Southwest Jiaotong University,2013(in Chinese).
[8]潘智俊,张焰.城市电网结构坚强性评价的指标体系和方法[J].中国电机工程学报,2015,35(16):3999-4005.Pan Zhijun,Zhang Yan.Index system and method for structure strength assessment of urban power network[J].Proceedings of the CSEE,2015,35(16):3999-4005(in Chinese).
[9]代杰杰,宋辉,杨祎,等.基于栈式降噪自编码器的输变电设备状态数据清洗方法[J].电力系统自动化,2017,41(12):224-230.Dai Jiejie,Song Hui,Yang Yi,et al.Cleaning method for status data of power transmission and transformation equipment based on stacked denoising autoencoders[J].Automation of Electric Power Systems,2017,41(12):224-230(in Chinese).
[10]王守相,陈海文,李小平,等.风电和光伏随机场景生成的条件变分自动编码器方法[J].电网技术,2018,42(6):1860-1867.Wang Shouxiang,Chen Haiwen,Li Xiaoping,et al.Conditional variational automatic encoder method for stochastic scenario generation of wind power and photovoltaic system[J].Power System Technology,2018,42(6):1860-1867(in Chinese).
[11]林刚,王波,彭辉,等.基于强泛化卷积神经网络的输电线路图像覆冰厚度辨识[J].中国电机工程学报,2018,38(11):3393-3401.Lin Gang,Wang Bo,Peng Hui,et al.Identification of icing thickness of transmission line based on strongly generalized convolutional neural network[J].Proceedings of the CSEE,2018,38(11):3393-3401(in Chinese).
[12]阚博文,Andrew Cui,朱文东,等.基于深度学习的绝缘开关图像分类[J].电力信息与通信技术,2018,16(3):8-15.Kan Bowen,Andrew Cui,Zhu Wendong,et al.Classifying images of isolator switch using convolutional neural networks[J].Electric Power Information and Communication Technology,2018,16(3):8-15(in Chinese).
[13]魏东,龚庆武,来文青,等.基于卷积神经网络的输电线路区内外故障判断及故障选相方法研究[J].中国电机工程学报,2016,36(S1):21-28.Wei Dong,Gong Qingwu,Lai Wenqing,et al.Research on internal and external fault diagnosis and fault-selection of transmission line based on convolutional neural network[J].Proceedings of the CSEE,2016,36(S1):21-28(in Chinese).
[14]刘威,张东霞,王新迎,等.基于深度强化学习的电网紧急控制策略研究[J].中国电机工程学报,2018,38(1):109-119.Liu Wei,Zhang Dongxia,Wang Xinying,et al.A decision making strategy for generating unit tripping under emergency circumstances based on deep reinforcement learning[J].Proceedings of the CSEE,2018,38(1):109-119(in Chinese).
[15]胡丽娟,刘科研,盛万兴,等.含随机出力分布式电源的配电网静态电压稳定快速概率评估方法[J].电网技术,2014,38(10):2766-2771.Hu Lijuan,Liu Keyan,Sheng Wanxing,et al.Fast probabilistic evaluation of static voltage stability in active distribution network considering random output from distributed generations[J].Power System Technology,2014,38(10):2766-2771(in Chinese).
[16]张李盈,范明天.配电网综合规划模型与算法的研究[J].中国电机工程学报,2004,24(6):59-64.Zhang Liying,Fan Mingtian.A new model and methodology for distribution network integration planning[J].Proceedings of the CSEE,2004,24(6):59-64(in Chinese).
[17]马志远,刘锋,沈沉,等.基于PageRank改进算法的电网脆弱线路快速辨识(一):理论基础[J].中国电机工程学报,2016,36(23):6363-6370.Ma Zhiyuan,Liu Feng,Shen Chen,et al.Rapid identification of vulnerable lines in power grid using modified PageRank algorithm(Part I):theoretical foundation[J].Proceedings of the CSEE,2016,36(23):6363-6370(in Chinese).
[18]马志远,刘锋,沈沉,等.基于PageRank改进算法的电网脆弱线路快速辨识(二):影响因素分析[J].中国电机工程学报,2017,37(1):36-44.Ma Zhiyuan,Liu Feng,Shen Chen,et al.Rapid identification of vulnerable lines in power grid using modified PageRank algorithm(Part II):factors affecting identification results[J].Proceedings of the CSEE,2017,37(1):36-44(in Chinese).
[19]Li Canbing,Liu Wencan,Cao Yijia,et al.Method for evaluating the importance of power grid nodes based on PageRank algorithm[J].IET Generation,Transmission&Distribution,2014,8(11):1843-1847.
[20]Krizhevsky A,Sutskever I,Hinton G E.ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems.Lake Tahoe,Nevada:ACM,2012:1097-1105.
[21]Billinton R,Wangdee W.Delivery point reliability indices of a bulk electric system using sequential Monte Carlo simulation[J].IEEE Transactions on Power Delivery,2006,21(1):345-352.
[22]Ioffe S,Szegedy C.Batch normalization:accelerating deep network training by reducing internal covariate shift[C]//Proceedings of the 32nd International Conference on Machine Learning.Lille,France:JMLR,2015.
[23]Lin Min,Chen Qiang,Yan Shuicheng.Network in network[J].arXiv preprint arXiv:1312.4400,2013.
[24]He Kaiming,Zhang Xiangyu,Ren Shaoqing,et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[C]//Proceedings of the 13th European Conference on Computer Vision.Zurich,Switzerland:Springer,2014.
[25]刘慧.基于KNN的中文文本分类算法研究[D].成都:西南交通大学,2010.Liu Hui.Research of Chinese text classification based on KNN[D].Chengdu:Southwest Jiaotong University,2010(in Chinese).
[26]王贺,胡志坚,张翌晖,等.基于聚类经验模态分解和最小二乘支持向量机的短期风速组合预测[J].电工技术学报,2014,29(4):237-245.Wang He,Hu Zhijian,Zhang Yihui,et al.A hybrid model for short-term wind speed forecasting based on ensemble empirical mode decomposition and least squares support vector machines[J].Transactions of China Electrotechnical Society,2014,29(4):237-245(in Chinese).
[27]Pan Zhijun,Zhang Yan.A novel centralized charging station planning strategy considering urban power network structure strength[J].Electric Power Systems Research,2016,136:100-109.