范士雄;李立新;王松岩;刘幸蔚;於益军;郝博文;

• 1:电力调度自动化技术研究与系统评价北京市重点实验室(中国电力科学研究院有限公司)
• 2:哈尔滨工业大学电气工程及自动化学院

摘要(Abstract)：

近年来,以深度学习为代表的先进人工智能技术促进了各行业的智能化发展。电网调控作为人工智能技术应用的重要领域之一,亟需借鉴互联网思维,充分利用人工智能技术,进一步提升电网调控业务的智能化水平。分析总结了人工智能技术的发展脉络,重点介绍了引发新一代人工智能技术大跨越的深度学习技术。聚焦大电网调控领域,论述了其对人工智能技术的需求分析。在此基础上,分析了人工智能技术在电网故障辨识、负荷预测、电网智能辅助决策和人机交互应用等方面的典型应用场景。最后通过电网故障辨识算例,进行了深度学习技术在电网调控应用的探索,可为调控业务智能化研究与发展提供有益的参考和借鉴。

关键词(KeyWords)： 电网调控;人工智能;深度学习;深度强化学习;态势感知;故障诊断

Abstract:

Keywords:

基金项目(Foundation): 国家电网有限公司科技项目“基于人工智能技术的电网调控体系架构及电网无功电压控制典型应用技术研究”(DZ71-18-013)~~

作者(Author): 范士雄;李立新;王松岩;刘幸蔚;於益军;郝博文;

Email:

DOI:

参考文献(References)：

• [1]朱永利，尹金良．人工智能在电力系统中的应用研究与实践综述[J]．发电技术，2018,39(2):106-111.Zhu Yongli,Yin Jinliang.Review of research and practice of artificial intelligence application in power systems[J]. Power Generation Technology,2018,39(2):106-111(in Chinese).
• [2]王耀瑜，张伯明，孙宏斌，等．一种基于专家知识的电力系统电压/无功分级分布式优化控制分区方法[J]．中国电机工程学报，1998,18(3):221-224.Wang Yaoyu,Zhang Boming,Song Hongbin,et al.A expert knowledge based subarea division method for hierarchical and distributed electric power system voltage/var optimization and control[J].Proceedings of the CSEE,1998,18(3):221-224(in Chinese).
• [3]杨争林，孙雅明．基于ANN的变电站电压和无功综合自动控制[J]．电力系统自动化，1999,23(13):10-13.Yang Zhenglin,Sun Yaming.The synthetic and automatic control of substation voltage and reactive power based on ANN[J].Automation of Electric Power Systems,1999,23(13):10-13(in Chinese).
• [4]吕志来，张保会．基于ANN和模糊控制相结合的电力负荷短期预测方法[J]．电力系统自动化，1999,23(22):37-39.LüZhilai,Zhang Baohui.Short-term load forecasting method based on combination of ANN and fuzzy control[J].Automation of Electric Power Systems,1999,23(22):37-39(in Chinese).
• [5] Aygen Z E,Seker S,Bagnyanik M,et al,Fault section estimation in electrical power systems using artificial neural network approach[C]//IEEE Transmission and Distribution Conference.New Orleans,LA,USA:IEEE,1999:466-469.
• [6]张武军，叶剑锋，梁伟杰，等．基于改进遗传算法的多目标无功优化[J]．电网技术，2004,28(11):67-71.Zhang Wujun,Ye Jianfeng,Liang Weijie,et al.Multiple-objective reactive power optimization based on improved genetic algorithm[J].Power System Technology,2004,28(11):67-71(in Chinese).
• [7]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).
• [8]张孝顺，李清，余涛，等．基于协同一致性迁移Q学习算法的虚拟发电部落AGC功率动态分配[J]．中国电机工程学报，2017,37(5):1455-1467.Zhang Xiaoshun,Li Qing,Yu Tao,et al.Collaborative consensus transfer Q-learning based dynamic generation dispatch of automatic generation control with virtual generation tribe[J].Proceedings of the CSEE,2017,37(5):1455-1467(in Chinese).
• [9]李婷，刘明波．求解多目标协调二级电压控制的简化强化学习方法[J]．中国电机工程学报，2013,33(31):130-139.LI Ting,Liu Mingbo.A reduced reinforcement learning method applied to multi-objective coordinated secondary voltage control[J].Proceedings of the CSEE,2013,33(31):130-139(in Chinese).
• [10]尚宇炜，马钊，彭晨阳，等．内嵌专业知识和经验的机器学习方法探索(一)：引导学习的提出与理论基础[J]．中国电机工程学报，2017,37(19):5560-5571.Shang Yuwei,Ma Zhao,Peng Chenyang,et al.Study of a novel machine learning method embedding expertise(part I):proposals and fundamentals of guiding learning[J].Proceedings of the CSEE.2017,37(19):5560-5571(in Chinese).
• [11]尚宇炜，马钊，彭晨阳，等．内嵌专业知识和经验的机器学习方法探索(二)：引导学习的应用与实践[J]．中国电机工程学报，2017,37(20):5852-5861.Shang Yuwei,Ma Zhao,Peng Chenyang,et al.Study of a novel machine learning method embedding expertise(part II):applications and practices of guiding learning[J].Proceedings of the CSEE.2017,37(20):5852-5861(in Chinese).
• [12]杨挺，赵黎媛，王成山．人工智能在电力系统及综合能源系统中的应用综述[J]．电力系统自动化，2019,43(1):2-14.Yang Ting,Zhao Liyuan,Wang Chengshan.Review on application of artificial intelligence in power system and integrated energy system[J].Automation of Electric Power Systems,2019,43(1):2-14(in Chinese).
• [13]李博，高志远．人工智能技术在智能电网中的应用分析和展望[J]．中国电力，2017,50(12):136-140.Li Bo,Gao Zhiyuan.Analysis and prospect on the application of artificial intelligence technologies in smart grid[J].Electric Power,2017,50(12):136-140(in Chinese).
• [14]闪鑫，陆晓，翟明玉，等．人工智能应用于电网调控的关键技术分析[J]．电力系统自动化，2019,43(1):49-57.Shan Xin,Lu Xiao,Zhai Mingyu,et al.Analysis of key technologies for artificial intelligence applied to power grid dispatch and control[J].Automation of Electric Power Systems,2019,43(1):49-57(in Chinese).
• [15]尚宇炜，郭剑波，吴文传，等．电力脑初探：一种多模态自适应学习系统[J]．中国电机工程学报，2018,38(11):3133-3143.Shang Yuwei,Guo Jianbo,Wu Wenchuan,et al.Preliminary study of electric power brain:a multimodal adaptive learning system[J].Proceedings of the CSEE,2018,38(11):3133-3143(in Chinese).
• [16] Yongli Zhu,Yang Y H,Hogg B W,et al.An expert system for power systems fault analysis[J].IEEE Transactions on Power Systems,1994,9(1):503-509.
• [17]史德明，李林川，宋建文．基于灰色预测和神经网络的电力系统负荷预测[J]．电网技术，2001,25(12):14-17.Shi Deming, Li Linchuan, Song Jianwen. Power system load forecasting based upon combination of grey forecast and artificial neural network[J].Power System Technology,2001,25(12):14-17(in Chinese).
• [18]武新宇，程春田，廖胜利，等．两阶段粒子群算法在水电站群优化调度中的应用[J]．电网技术，2006,30(20):25-28.Wu Xinyu,Chen Chuntian,Liao Shengli,et al.Application of twostage particle swarm optimization algorithm in optimized dispatching of hydropower station group[J].Power System Technology,2006,30(20):25-28(in Chinese).
• [19] Corinna C, Vladimir V. Support-vector networks[J]. Machine Learning,1995,20(3):273-297.
• [20] Chen Y,Wang G,Dong S.Learning with progressive transductive support vector machine[J].Pattern Recognition Letters,2003,24(12):1845-1855.
• [21] Turian J,Ratinov L,Bengio Y.Word representations:a simple and general method for semi-supervised learning[C]//ACL 2010-48th Annual Meeting of the Association for Computational Linguistics. Uppsala, Sweden:Association for Computational Linguistics(ACL),2010:384-394.
• [22] Hu B,Lu Z,Li H,et al.Convolutional neural network architectures for matching natural language sentences[J].Advances In Neural Information Processing Systems,2015,3(1):2042-2050.
• [23] Hinton G E,Osindero S,Teh Y W.A fast learning algorithm for deep belief nets[J].Neural Computation,2006,18(7):1527-1554.
• [24]朱乔木，李弘毅，王子琪，等．基于长短期记忆网络的风电场发电功率超短期预测[J]．电网技术，2017,41(12):3797-3802.Zhu Qiaomu,Li Hongyi,Wang Ziqi,et al.Short-term wind power forecasting based on LSTM[J].Power System Technology,2017,41(12):3797-3802(in Chinese).
• [25] Lecun Y L,Bottou L,Bengio Y,et al.Gradient-based learning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324.
• [26] Krizhevsky A,Sutskever I,Hinton G.ImageNet classification with deep convolutional neural networks[C]//26th Annual Conference on Neural Information Processing Systems.Lake Tahoe,NV,United States:Neural Information Processing Systems Foundation,2012:1097-1105.
• [27] Baidu大脑AI开放平台．EasyDL升级输电线路外部隐患检查方案[EB/OL].[2019-06-12].http://ai.baidu.com/customer/nfdw.
• [28] Lee H J,Ahn B S,Park Y M.A fault diagnosis expert system for distribution substations[J].IEEE Transactions on Power Delivery,2000,15(1):92-97.
• [29] Sun J,Qin S,Song Y.Fault diagnosis of electric power systems based on fuzzy petri nets[J].IEEE Transactions on Power System,2004,19(4):2053-2059.
• [30] Luo X,Kezunovic M.Implementing fuzzy reasoning petri-nets for fault section estimation[J].IEEE Transactions on Power Delivery,2008,23(2):676-685.
• [31] Yongli Z,Limin H,Jinling L.Bayesian networks-based approach for power systems fault diagnosis[J]. IEEE Transactions on Power Delivery,2006,21(2):634-639.
• [32] Aécio L O,Olinto C B de A,Ghendy C J,et al.A mixed integer programming model for optimal fault section estimation in power systems[J].International Journal of Electrical Power and Energy Systems,2016,77(5):372-384.
• [33] Chen W,Tsai S,Lin H.Fault section estimation for power networks using logic cause-effect models[J].IEEE Transactions on Power Delivery,2011,26(2):963-971.
• [34]郭创新，朱传柏，曹一家，等．电力系统故障诊断的研究现状与发展趋势[J]．电力系统自动化，2006,30(8):98-103.Guo Chuangxin,Zhu Chuanbai,Cao Yijia,et al.State of arts of fault diagnosis of power systems[J]. Automation of Electric Power Systems,2006,30(8):98-103(in Chinese).
• [35] dos Santos F W A,Bezerra U H,Nunes M VA,et al.Simultaneous fault section estimation and protective device failure detection using percentage values of the protective devices alarms[J]. IEEE Transactions on Power System,2013,28(1):170-180.
• [36] Bi T,Yan Z,Wen F,et al.On-line fault section estimation in power systems with radial basis function neural network[J].International Journal of Electrical Power and Energy Systems,2002,24(4):321-328.
• [37] Cardoso G,Rolim J G,Zurn H H.Application of neural-network modules to electric power system fault section estimation[J].IEEE Transactions on Power Delivery,2004,19(3):1034-1041.
• [38] Salehi-Dobakhshari A,Ranjbar A M.Application of synchronized phasor measurements to wide-area fault diagnosis and location[J].IET Generation,Transmission&Distribution,2014,8(4):716-729.
• [39]谢敏，吴亚雄，闫圆圆，等．基于改进动态自适应模糊Petri网与BP算法的电网故障诊断[J]．中国电机工程学报，2015,35(12):3008-3017.Xie Min,Wu Yaxiong,Yan Yuanyuan,et al.Power system fault diagnosis based on improved dynamic adaptive fuzzy petri nets and back propagation algorithm[J].Proceedings of the CSEE,2015,35(12):3008-3017(in Chinese).
• [40]陈彬，于继来．基于电网潮流分布特征的在线故障智能诊断[J]．电力系统自动化，2007,31(16):29-34.Chen Bin,Yu Jilai.Online fault intelligent diagnosis based on distributed characteristics of power flow with outage of electric power network[J].Automation of Electric Power Systems,2007,31(16):29-34(in Chinese).
• [41]于尔铿，刘广一，周京阳．能量管理系统(EMS)[M]．北京：科学出版社，1998.
• [42]侯广松，刘伟生，谢广建．基于相关因素映射和神经网络的短期负荷预测[J]．电网技术，2006,30(S2):553-556.Hou Guangsong,Liu Weisheng,Xie Guangjian.Short term load forecasting based on correlative factor mapping and neural networks[J].Power System Technology,2006,30(S2):553-556(in Chinese).
• [43]耿艳，韩学山，韩力．基于最小二乘支持向量机的短期负荷预测[J]．电网技术，2008,32(18):72-76.Geng Yan,Han Xueshan,Han Li.Short-term load forecasting based on least squares support vector machines[J]. Power System Technology,2008,32(18):72-76(in Chinese).
• [44]牛东晓，王建军，李莉，等．基于粗糙集和决策树的自适应神经网络短期负荷预测方法[J]．电力自动化设备，2009,29(10):30-34.Niu Dongxiao,Wang Jianjun,Li Li,et al.Short-term load forecasting using adaptive ANN based on rough set and decision tree[J].Electric Power Automation Equipment,2009,29(10):30-34(in Chinese).
• [45]李博，门德月，严亚勤，等．基于数值天气预报的母线负荷预测[J]．电力系统自动化，2015,39(1):137-140.Li Bo,Men Deyue,Yan Yaqin,et al.Bus load forecasting based on numerical weather prediction[J]. Automation of Electric Power Systems,2015,39(1):137-140(in Chinese).
• [46]孔祥玉，郑锋，鄂志君，等．基于深度信念网络的短期负荷预测方法[J]．电力系统自动化，2018,42(5):133-139.Kong Xiangyu,Zheng Feng,E Zhijun,et al.Short-term load forecasting based on deep belief network[J].Automation of Electric Power Systems,2018,42(5):133-139(in Chinese).
• [47]张宇帆，艾芊，林琳，等．基于深度长短时记忆网络的区域级超短期负荷预测方法[J]．电网技术，2019,43(6):1884-1892.Zhang Yufan,Ai Qian,Lin Lin,et al.A very short-term load forecasting method based on deep LSTM RNN at zone level[J].Power System Technology,2019,43(6):1884-1892(in Chinese).
• [48]陆继翔，张琪培，杨志宏，等．基于CNN-LSTM混合神经网络模型的短期负荷预测方法[J]．电力系统自动化，2019,43(8):131-137.Lu Jixiang,Zhang Qipei,Yang Zhihong,et al.Short-term load forecasting method based on hybrid CNN-LSTM neural network model[J].Automation of Electric Power Systems,2019,43(8):131-137(in Chinese).
• [49]邱剑，王慧芳，应高亮，等．文本信息挖掘技术及其在断路器全寿命状态评价中的应用[J]．电力系统自动化，2016,40(6):107-112.Qiu Jian,Wang Huifang,Ying Gaoliang,et al.Text mining technique and application of lifecycle condition assessment for circuit breaker[J].Automation of Electric Power Systems,2016,40(6):107-112(in Chinese).
• [50]李新鹏，徐建航，郭子明，等．调度自动化系统知识图谱的构建与应用[J]．中国电力，2019,52(2):70-77.Li Xinpeng,Xu Jianhang,Guo Ziming,et al.Construction and application of knowledge graph of power dispatching automation system[J].Electric Power,2019,52(2):70-77(in Chinese).
• [51]范俊君，田丰，杜一，等．智能时代人机交互的一些思考[J]．中国科学：信息科学，2018,48(4):361-375.Fan Junjun,Tian Feng,Du Yi,et al.Thoughts on human-computer interaction in the age of artificial intelligence[J].Scientia Sinica Informationis,2018,48(4):361-375(in Chinese).