人工智能在电力系统暂态问题中的应用综述
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摘要
现代智能电网出现了电力电子化、信息物理融合和大电网复杂互联等新特征,从而对电力系统暂态问题的分析与控制方法产生了极大影响。人工智能(artificialintelligence,AI)在解决数据问题中的优势与暂态问题特点匹配程度高。该文从信息、机理、仿真、分析和控制等角度分析了电力系统暂态问题出现的新特点,归纳总结了现有将AI用于分析电力系统暂态问题时的研究成果,指出了研究中仍需解决的问题,探讨了在数据获取、特征提取和算法应用等方面的若干改进思路,并对AI应用于暂态稳定问题的研究现状进行总结。
Control and analysis methods in power system transient stability assessment(TSA) evolved fundamentally with the trends of power electronic domination, cyber-physical integration and large-scale power system interconnection. In order to satisfy the urgent requirements of TSA, artificial intelligence(AI) with the advantage in data mining was widely studied. This paper analyzed new features in TSA from the aspects of information, theory, simulation, analysis and control in detail. Moreover, based on the review of progress in the field of applying AI to TSA, the existing problems in data source, sample generation and algorithm application were analyzed for further improvements. Finally, a conclusion on current research progress was made.
Control and analysis methods in power system transient stability assessment(TSA) evolved fundamentally with the trends of power electronic domination, cyber-physical integration and large-scale power system interconnection. In order to satisfy the urgent requirements of TSA, artificial intelligence(AI) with the advantage in data mining was widely studied. This paper analyzed new features in TSA from the aspects of information, theory, simulation, analysis and control in detail. Moreover, based on the review of progress in the field of applying AI to TSA, the existing problems in data source, sample generation and algorithm application were analyzed for further improvements. Finally, a conclusion on current research progress was made.
引文
[1]周孝信,陈树勇,鲁宗相.电网和电网技术发展的回顾与展望:试论三代电网[J].中国电机工程学报,2013,33(22):1-11.Zhou Xiaoxin,Chen Shuyong,Lu Zongxiang.Review and prospect for power system development and related technologies:a concept of three-generation power systems[J].Proceedings of the CSEE,2013,33(22):1-11(in Chinese).
[2]汤广福,庞辉,贺之渊.先进交直流输电技术在中国的发展与应用[J].中国电机工程学报,2016,36(7):1760-1771.Tang Guangfu,Pang Hui,He Zhiyuan.R&D and application of advanced power transmission technology in China[J].Proceedings of the CSEE,2016,36(7):1760-1771(in Chinese).
[3]朱蜀,刘开培,秦亮,等.电力电子化电力系统暂态稳定性分析综述[J].中国电机工程学报,2017,37(14):3948-3962.Zhu Shu,Liu Kaipei,Qin Liang,et al.Analysis of transient stability of power electronics dominated power system:an overview[J].Proceedings of the CSEE,2017,37(14):3948-3962(in Chinese).
[4]贺倩.人工智能技术的发展与应用[J].电力信息与通信技术,2017,15(9):32-37.He Qian.Development and application of artificial intelligence technology[J].Electric Power Information and Communication Technology,2017,15(9):32-37(in Chinese).
[5]赵俊华,文福拴,薛禹胜,等.电力CPS的架构及其实现技术与挑战[J].电力系统自动化,2010,34(16):1-7.Zhao Junhua,Wen Fushuan,Xue Yusheng,et al.Cyber physical power systems:architecture,implementation techniques and challenges[J].Automation of Electric Power Systems,2010,34(16):1-7(in Chinese).
[6]Akimoto Y,Tanaka H,Yoshizawa J,et al.Transient stability expert system[J].IEEE Transactions on Power Systems,1989,4(1):312-320.
[7]Fischl R,Kam M,Chow J C,et al.Screening power system contingencies using a back-propagation trained multiperceptron[C]//IEEE International Symposium on Circuits and Systems.Portland,America,1989.
[8]Lecun Y,Bengio Y,Hinton G.Deep learning[J].Nature,2015,521(7553):436-444.
[9]Silver D,Schrittwieser J,Simonyan K,et al.Mastering the game of Go without human knowledge[J].Nature,2017,550(7676):354-359.
[10]陈铉,阚博文,刘广一.GPU技术的最新进展及其在电力系统中的应用前景探讨[J].电力信息与通信技术,2018,16(3):16-25.Chen Xuan,Kan Bowen,Liu Guangyi.The latest development of GPU and its prospective application in power system[J].Electric Power Information and Communication Technology,2018,16(3):16-25(in Chinese).
[11]Zhao W,Xu W,Yang M,et al.Dual learning for crossdomain image captioning[C]//Proceedings of the 2017ACM on Conference on Information and Knowledge Management.New York,America,2017.
[12]Deng J,Dong W,Socher R,et al.Imagenet:A large-scale hierarchical image database[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition,Miami,America,2009.
[13]Wang B,Fang B,Wang Y,et al.Power system transient stability assessment based on big data and the core vector machine[J].IEEE Transactions on Smart Grid,2016,7(5):2561-2570.
[14]赵俊华,董朝阳,文福拴,等.面向能源系统的数据科学:理论、技术与展望[J].电力系统自动化,2017,41(4):1-11.Zhao Junhua,Dong Zhaoyang,Wen Fushuan,et al.Data science for energy systems:thoery,techniques and prospect[J].Automation of Electric Power Systems,2017,41(4):1-11(in Chinese).
[15]Bhui P,Senroy N.Real-time prediction and control of transient stability using transient energy function[J].IEEETransactions on Power Systems,2017,32(2):923-934.
[16]王建,熊小伏,梁允,等.地理气象相关的输电线路风险差异评价方法及指标[J].中国电机工程学报,2016,36(5):1252-1259.Wang Jian,Xiong Xiaofu,Liang Yun,et al.Geographical and meteorological factor related transmission line risk difference assessment:method and indexes[J].Proceedings of the CSEE,2016,36(5):1252-1259(in Chinese).
[17]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 thining:Part one big data and power big data[J].Automation of Electric Power Systems,2016,40(1):1-8(in Chinese).
[18]毕天姝,刘素梅,薛安成,等.逆变型新能源电源故障暂态特性分析[J].中国电机工程学报,2013,33(13):165-171.Bi Tianshu,Liu Sumei,Xue Ancheng,et al.Fault characteristics of inverter-interfaced renewable energy sources[J].Proceedings of the CSEE,2013,33(13):165-171(in Chinese).
[19]秦晓辉,张志强,徐征雄,等.基于准稳态模型的特高压半波长交流输电系统稳态特性与暂态稳定研究[J].中国电机工程学报,2011,31(31):66-76.Qin Xiaohui,Zhang Zhiqiang,Xu Zhengxiong,et al.Study on the steady state characteristic and transient stability of UHVAC half-wave-length transmission system based on quasi-steady model[J].Proceedings of the CSEE,2011,31(31):66-76(in Chinese).
[20]薛禹胜.电力市场稳定性与电力系统稳定性的相互影响[J].电力系统自动化,2002,26(21):1-6.Xue Yusheng.Interaction between power market stability and power system stability[J].Automation of Electric Power Systems,2002,26(21):1-6(in Chinese).
[21]汤奕,陈倩,李梦雅,等.电力信息物理融合系统环境中的网络攻击研究综述[J].电力系统自动化,2016,40(17):59-69.Tang Yi,Chen Qian,Li Mengya,et al.Overview on cyber-attacks against cyber physical power system[J].Automation of Electric Power Systems,2016,40(17):59-69(in Chinese).
[22]郭庆来,辛蜀骏,王剑辉,等.由乌克兰停电事件看信息能源系统综合安全评估[J].电力系统自动化,2016,40(5):145-147.Guo Qinglai,Xin Shujun,Wang Jianhui,et al.Comprehensive security assessment for a cyber physical energy system[J].Automation of Electric Power Systems,2016,40(5):145-147(in Chinese).
[23]郭庆来,辛蜀骏,孙宏斌,等.电力系统信息物理融合建模与综合安全评估:驱动力与研究构想[J].中国电机工程学报,2016,36(6):1481-1489.Guo Qinglai,Xin Shujun,Sun Hongbin,et al.Power system cyber-physical modelling and security assessment:motivation and ideas[J].Proceedings of the CSEE,2016,36(6):1481-1489(in Chinese).
[24]贺之渊,刘栋,庞辉.柔性直流与直流电网仿真技术研究[J].电网技术,2018,42(1):1-12.He Zhiyuan,Liu Dong,Pang Hui.Research of simulation technologies of VSC-HVDC and DC grids[J].Power System Technology,2018,42(1):1-12(in Chinese).
[25]谢小荣,刘华坤,贺静波,等.新能源发电并网系统的小信号阻抗/导纳网络建模方法[J].电力系统自动化,2017,41(12):26-32.Xie Xiaorong,Liu Huakun,He Jingbo,et al.Small-signal impedance/admittance network modeling for gridconnnected renewable energy generation systems[J].Automation of Electric Power Systems,2017,41(12):26-32(in Chinese).
[26]黄彦浩,于之虹,谢昶,等.电力大数据技术与电力系统仿真计算结合问题研究[J].中国电机工程学报,2015,35(1):13-22.Huang Yanhao,Yu Zhihong,Xie Chang,et al.Study on the application of electric power big data technology in power system simulation[J].Proceedings of the CSEE,2015,35(1):13-22(in Chinese).
[27]汤涌.基于响应的电力系统广域安全稳定控制[J].中国电机工程学报,2014,34(29):5041-5050.Tang Yong.Response-based wide area control for power system security and stability[J].Proceedings of the CSEE,2014,34(29):5041-5050(in Chinese).
[28]刘强,石立宝,周明,等.现代电力系统恢复控制研究综述[J].电力自动化设备,2007(11):104-110.Liu Qiang,Shi Libao,Zhou Ming,et al.Survey of power system restoration control[J].Electric Power Automation Equipment,2007(11):104-110(in Chinese).
[29]汪震,宋晓喆,杨正清,等.考虑暂态安全的预防-紧急协调控制问题研究[J].中国电机工程学报,2014,34(34):6159-6166.Wang Zheng,Song Xiaozhe,Yang Zhengqing,et al.Acoordinated preventive and emergency control considering system transient security[J].Proceedings of the CSEE,2014,34(34):6159-6166(in Chinese).
[30]兰洲,倪以信,甘德强.现代电力系统暂态稳定控制研究综述[J].电网技术,2005,29(15):40-50.Lan Zhou,Ni Yixin,Gan Deqiang.A survey on transient stability control of modern power systems[J].Power System Technology,2005,29(15):40-50(in Chinese).
[31]许洪强,姚建国,南贵林,等.未来电网调度控制系统应用功能的新特征[J].电力系统自动化,2018,42(1):1-7.Xu Hongqiang,Yao Jianguo,Nan Guilin,et al.New features of application function for future dispatching and control systems[J].Automation of Electric Power System,2018,42(1):1-7(in Chinese).
[32]童晓阳,叶圣永.数据挖掘在电力系统暂态稳定评估中的应用综述[J].电网技术,2009,33(20):88-93.Tong Xiaoyang,Ye Shengyong.A survey on application of data mining in transient stability assessment of power system[J].Power System Technology,2009,33(20):88-93(in Chinese).
[33]姚德全,贾宏杰,赵帅.基于复合神经网络的电力系统暂态稳定评估和裕度预测[J].电力系统自动化,2013,37(20):41-46.Yao Dequan,Jia Hongjie,Zhao Shuai.Power system transient stability assessment and stability margin prediction based on compound neural network[J].Automation of Electric Power Systems,2013,37(20):41-46(in Chinese).
[34]田芳,周孝信,于之虹.基于支持向量机综合分类模型和关键样本集的电力系统暂态稳定评估[J].电力系统保护与控制,2017,45(22):1-8.Tian Fang,Zhou Xiaoxin,Yu Zhihong.Power system transient stability assessment based on comprehensive SVM classification model and key sample set[J].Power System Protection and Control,2017,45(22):1-8(in Chinese).
[35]Gomez F R,Rajapakse A D,Annakkage U D,et al.Support vector machine-based algorithm for post-fault transient stability status prediction using synchronized measurements[J].IEEE Transactions on Power Systems,2011,26(3):1474-1483.
[36]周艳真,吴俊勇,冀鲁豫,等.基于两阶段支持向量机的电力系统暂态稳定预测及预防控制[J].中国电机工程学报,2018,38(1):137-147.Zhou Yanzhen,Wu Junyong,Ji Luyu,et al.Two-stage support vector machines for transient stability prediction and preventive control of power systems[J].Proceedings of the CSEE,2018,38(1):137-147(in Chinese).
[37]Guo T,Milanovic J V.Probabilistic framework for assessing the accuracy of data mining tool for online prediction of transient stability[J].IEEE Transactions on Power Systems,2013,29(1):377-385.
[38]李兆伟,吴雪莲,庄侃沁,等.“9·19”锦苏直流双极闭锁事故华东电网频率特性分析及思考[J].电力系统自动化,2017,41(7):149-155.Li Zhaowei,Wu Xuelian,Zhuang Kanqin,et al.Analysis and reflection on frequency characteristics of east China grid after bipolar locking of“9·19”Jinping-Sunan DCtransmission line[J].Automation of Electric Power Systems,2017,41(7):149-155(in Chinese).
[39]汤奕,王琦,倪明,等.电力和信息通信系统混合仿真方法综述[J].电力系统自动化,2015,39(23):33-42.Tang Yi,Wang Qi,Ni Ming,et al.Review on the hybrid simulation method for power and communication system[J].Automation of Electric Power Systems,2015,39(23):33-42(in Chinese).
[40]Tang Y,Cui H,Wang Q.Prediction model of the power system frequency using a cross-entropy ensemble algorithm[J].Entropy,2017,19(10):552.
[41]Zhang Y,Xu Y,Dong Z Y,et al.Intelligent early warning of power system dynamic insecurity risk:toward optimal accuracy-earliness tradeoff[J].IEEE Transactions on Industrial Informatics,2017,13(5):2544-2554.
[42]Zheng C,Malbasa V,Kezunovic M.Regression tree for stability margin prediction using synchrophasor measurements[J].IEEE Transactions on Power Systems,2013,28(2):1978-1987.
[43]管霖,曹绍杰.基于人工智能的大系统分层在线暂态稳定评估[J].电力系统自动化,2000,24(2):22-26.Guan Lin,Tso S K.Combination of heuristic reasoning and ANN to realize on-line transient stability assessment in large scale power systems[J].Automation of Electric Power Systems,2000,24(2):22-26(in Chinese).
[44]朱乔木,党杰,陈金富,等.基于深度置信网络的电力系统暂态稳定评估方法[J].中国电机工程学报,2018,38(3):735-743.Zhu Qiaomu,Dang Jie,Chen Jinfu,et al.A method for power system transient stability assessment based on deep belief networks[J].Proceedings of the CSEE,2018,38(3):735-743(in Chinese).
[45]顾雪平,李扬,吴献吉.基于局部学习机和细菌群体趋药性算法的电力系统暂态稳定评估[J].电工技术学报,2013,28(10):271-279.Gu Xueping,Li Yang,Wu Xianji.Transient stability assessment of power systems based on local learning machine and bacterial colony chemotaxis algorithm[J].Transactions of China Electrotechnical Society,2013,28(10):271-279(in Chinese).
[46]叶圣永,王晓茹,刘志刚,等.基于受扰严重机组特征及机器学习方法的电力系统暂态稳定评估[J].中国电机工程学报,2011,31(1):46-51.Ye Shengyong,Wang Xiaoru,Liu Zhigang,et al.Power system transient stability assessment based on severely distributed generator attributes and machine learning method[J].Proceedings of the CSEE,2011,31(1):46-51(in Chinese).
[47]唐飞,王波,查晓明,等.基于双阶段并行隐马尔科夫模型的电力系统暂态稳定评估[J].中国电机工程学报,2013,33(10):90-97+14.Tang Fei,Wang Bo,Zha Xiaoming,et al.Power system transient stability assessment based on two-stage parallel hidden Markov model[J].Proceedings of the CSEE,2013,33(10):90-97+14(in Chinese).
[48]Jieping Ye,Ravi Janardan,Qi Li,et al.Feature reduction via generalized uncorrelated linear discriminant analysis[J].IEEE Transactions on Knowledge and Data Engineering,2006,18(10):1312-1322.
[49]顾雪平,曹绍杰,张文勤.人工神经网络和短时仿真结合的暂态安全评估事故筛选方法[J].电力系统自动化,1999,23(8):16-19.Gu Xueping,S K Tso,Zhang Wenqing.Integration of ANNs and short-duration numerical simulation for contingency screening of transient security assessment[J].Automation of Electric Power Systems,1999,23(8):16-19(in Chinese).
[50]刘艳,顾雪平,李军.用于暂态稳定评估的人工神经网络输入特征离散化方法[J].中国电机工程学报,2005,25(15):56-61.Liu Yan,Gu Xueping,Li Jun.Discretization in artificial neural networks used for transient stability assessment[J].Proceedings of the CSEE,2005,25(15):56-61(in Chinese).
[51]Amjady N,Majedi S F.Transient stability prediction by a hybrid intelligent system[J].IEEE Transactions on Power Systems,2007,22(3):1275-1283.
[52]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(in Chinese).
[53]叶圣永,王晓茹,刘志刚,等.基于支持向量机增量学习的电力系统暂态稳定评估[J].电力系统自动化,2011,35(11):15-19.Ye Shengyong,Wang Xiaoru,Liu Zhigang,et al.Power system transient stability assessment based on support vector machine incremental learning method[J].Automation of Electric Power Systems,2011,35(11):15-19(in Chinese).
[54]He M,Zhang J,Vittal V.Robust online dynamic security assessment using adaptive ensemble decision-tree learning[J].IEEE Transactions on Power Systems,2013,28(4):4089-4098.
[55]卢锦玲,朱永利,赵洪山,等.提升型贝叶斯分类器在电力系统暂态稳定评估中的应用[J].电工技术学报,2009,24(5):177-182.Lu Jinling,Zhu Yongli,Zhao Hongshan,et al.Power system transient stability assessment based on boosting Bayesian classifier[J].Transactions of China Electrotechnical Society,2009,24(5):177-182(in Chinese).
[56]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).
[57]胡伟,郑乐,闵勇,等.基于深度学习的电力系统故障后暂态稳定评估研究[J].电网技术,2017,41(10):3140-3146(in Chinese).Hu Wei,Zheng Le,Min Yong,et al.Research on power system transient stability assessment based on deep learning of big data technique[J].Power System Technology,2017,41(10):3140-3146(in Chinese).
[58]Hadidi R,Jeyasurya B.Reinforcement learning based real-time wide-area stabilizing control agents to enhance power system stability[J].IEEE Transactions on Smart Grid,2013,4(1):489-497.
[59]段青,赵建国,马艳.基于稀疏贝叶斯学习的电力系统暂态稳定评估[J].电力自动化设备,2009,29(9):36-40.Duan Qing,Zhao Jianguo,Ma Yan.Power systems transient stability assessment based on sparse Bayesian learning[J].Electric Power Automation Equipment,2009,29(9):36-40(in Chinese).
[60]Amraee T,Ranjbar S.Transient instability prediction using decision tree technique[J].IEEE Transactions on Power Systems,2013,28(3):3028-3037.
[61]Zheng C,Malbasa V,Kezunovic M.Regression tree for stability margin prediction using synchrophasor measurements[J].IEEE Transactions on Power Systems,2013,28(2):1978-1987.
[62]Goodfellow I J,Pouget-Abadie J,Mirza M,et al.Generative adversarial nets[J].Advances in Neural Information Processing Systems,2014:2672-2680.
[63]Cutler M,Walsh T J,How J P.Real-world reinforcement learning via multifidelity simulators[J].IEEE Transactions on Robotics,2017,31(3):655-671.
[64]Jouppi N P,Young C,Patil N,et al.In-datacenter performance analysis of a tensor processing unit[C]//Proceedings of the 44th Annual International Symposium on Computer Architecture.Toronto,Canada,2017.
[65]薛禹胜.因果分析及机器学习之间的壁垒与融合[R].北京:中国电力科学研究院,北京,2016.Xue Yusheng.Barrier and integration between causal analysis and machine learning[R].Beijing:China Electric Power Research Institute,2016(in Chinese).
[66]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+5833.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+5833(in Chinese).
[67]Wang J,Zhong H,Lai X,et al.Exploring key weather factors from analytical modeling toward improved solar power forecasting[J].IEEE Transactions on Smart Grid,2017,PP(99):1-1.
[68]王琦,李峰,汤奕,等.基于物理-数据融合模型的电网暂态频率特征在线预测方法[J].电力系统自动化,2018,42(19):1-9.Wang Qi,Li Feng,Tang Yi,et al.Automation of Electric Power Systems,2018,42(19):1-9(in Chinese).
[69]董娜,刘伟娜,侯波涛.基于大数据的网络异常行为建模方法[J].电力信息与通信技术,2018,16(1):6-10.Dong Na,Liu Weina,Hou Botao.Modeling method of network abnormal behavior based on big data[J].Electric Power Information and Communication Technology,2018,16(1):6-10(in Chinese).
[2]汤广福,庞辉,贺之渊.先进交直流输电技术在中国的发展与应用[J].中国电机工程学报,2016,36(7):1760-1771.Tang Guangfu,Pang Hui,He Zhiyuan.R&D and application of advanced power transmission technology in China[J].Proceedings of the CSEE,2016,36(7):1760-1771(in Chinese).
[3]朱蜀,刘开培,秦亮,等.电力电子化电力系统暂态稳定性分析综述[J].中国电机工程学报,2017,37(14):3948-3962.Zhu Shu,Liu Kaipei,Qin Liang,et al.Analysis of transient stability of power electronics dominated power system:an overview[J].Proceedings of the CSEE,2017,37(14):3948-3962(in Chinese).
[4]贺倩.人工智能技术的发展与应用[J].电力信息与通信技术,2017,15(9):32-37.He Qian.Development and application of artificial intelligence technology[J].Electric Power Information and Communication Technology,2017,15(9):32-37(in Chinese).
[5]赵俊华,文福拴,薛禹胜,等.电力CPS的架构及其实现技术与挑战[J].电力系统自动化,2010,34(16):1-7.Zhao Junhua,Wen Fushuan,Xue Yusheng,et al.Cyber physical power systems:architecture,implementation techniques and challenges[J].Automation of Electric Power Systems,2010,34(16):1-7(in Chinese).
[6]Akimoto Y,Tanaka H,Yoshizawa J,et al.Transient stability expert system[J].IEEE Transactions on Power Systems,1989,4(1):312-320.
[7]Fischl R,Kam M,Chow J C,et al.Screening power system contingencies using a back-propagation trained multiperceptron[C]//IEEE International Symposium on Circuits and Systems.Portland,America,1989.
[8]Lecun Y,Bengio Y,Hinton G.Deep learning[J].Nature,2015,521(7553):436-444.
[9]Silver D,Schrittwieser J,Simonyan K,et al.Mastering the game of Go without human knowledge[J].Nature,2017,550(7676):354-359.
[10]陈铉,阚博文,刘广一.GPU技术的最新进展及其在电力系统中的应用前景探讨[J].电力信息与通信技术,2018,16(3):16-25.Chen Xuan,Kan Bowen,Liu Guangyi.The latest development of GPU and its prospective application in power system[J].Electric Power Information and Communication Technology,2018,16(3):16-25(in Chinese).
[11]Zhao W,Xu W,Yang M,et al.Dual learning for crossdomain image captioning[C]//Proceedings of the 2017ACM on Conference on Information and Knowledge Management.New York,America,2017.
[12]Deng J,Dong W,Socher R,et al.Imagenet:A large-scale hierarchical image database[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition,Miami,America,2009.
[13]Wang B,Fang B,Wang Y,et al.Power system transient stability assessment based on big data and the core vector machine[J].IEEE Transactions on Smart Grid,2016,7(5):2561-2570.
[14]赵俊华,董朝阳,文福拴,等.面向能源系统的数据科学:理论、技术与展望[J].电力系统自动化,2017,41(4):1-11.Zhao Junhua,Dong Zhaoyang,Wen Fushuan,et al.Data science for energy systems:thoery,techniques and prospect[J].Automation of Electric Power Systems,2017,41(4):1-11(in Chinese).
[15]Bhui P,Senroy N.Real-time prediction and control of transient stability using transient energy function[J].IEEETransactions on Power Systems,2017,32(2):923-934.
[16]王建,熊小伏,梁允,等.地理气象相关的输电线路风险差异评价方法及指标[J].中国电机工程学报,2016,36(5):1252-1259.Wang Jian,Xiong Xiaofu,Liang Yun,et al.Geographical and meteorological factor related transmission line risk difference assessment:method and indexes[J].Proceedings of the CSEE,2016,36(5):1252-1259(in Chinese).
[17]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 thining:Part one big data and power big data[J].Automation of Electric Power Systems,2016,40(1):1-8(in Chinese).
[18]毕天姝,刘素梅,薛安成,等.逆变型新能源电源故障暂态特性分析[J].中国电机工程学报,2013,33(13):165-171.Bi Tianshu,Liu Sumei,Xue Ancheng,et al.Fault characteristics of inverter-interfaced renewable energy sources[J].Proceedings of the CSEE,2013,33(13):165-171(in Chinese).
[19]秦晓辉,张志强,徐征雄,等.基于准稳态模型的特高压半波长交流输电系统稳态特性与暂态稳定研究[J].中国电机工程学报,2011,31(31):66-76.Qin Xiaohui,Zhang Zhiqiang,Xu Zhengxiong,et al.Study on the steady state characteristic and transient stability of UHVAC half-wave-length transmission system based on quasi-steady model[J].Proceedings of the CSEE,2011,31(31):66-76(in Chinese).
[20]薛禹胜.电力市场稳定性与电力系统稳定性的相互影响[J].电力系统自动化,2002,26(21):1-6.Xue Yusheng.Interaction between power market stability and power system stability[J].Automation of Electric Power Systems,2002,26(21):1-6(in Chinese).
[21]汤奕,陈倩,李梦雅,等.电力信息物理融合系统环境中的网络攻击研究综述[J].电力系统自动化,2016,40(17):59-69.Tang Yi,Chen Qian,Li Mengya,et al.Overview on cyber-attacks against cyber physical power system[J].Automation of Electric Power Systems,2016,40(17):59-69(in Chinese).
[22]郭庆来,辛蜀骏,王剑辉,等.由乌克兰停电事件看信息能源系统综合安全评估[J].电力系统自动化,2016,40(5):145-147.Guo Qinglai,Xin Shujun,Wang Jianhui,et al.Comprehensive security assessment for a cyber physical energy system[J].Automation of Electric Power Systems,2016,40(5):145-147(in Chinese).
[23]郭庆来,辛蜀骏,孙宏斌,等.电力系统信息物理融合建模与综合安全评估:驱动力与研究构想[J].中国电机工程学报,2016,36(6):1481-1489.Guo Qinglai,Xin Shujun,Sun Hongbin,et al.Power system cyber-physical modelling and security assessment:motivation and ideas[J].Proceedings of the CSEE,2016,36(6):1481-1489(in Chinese).
[24]贺之渊,刘栋,庞辉.柔性直流与直流电网仿真技术研究[J].电网技术,2018,42(1):1-12.He Zhiyuan,Liu Dong,Pang Hui.Research of simulation technologies of VSC-HVDC and DC grids[J].Power System Technology,2018,42(1):1-12(in Chinese).
[25]谢小荣,刘华坤,贺静波,等.新能源发电并网系统的小信号阻抗/导纳网络建模方法[J].电力系统自动化,2017,41(12):26-32.Xie Xiaorong,Liu Huakun,He Jingbo,et al.Small-signal impedance/admittance network modeling for gridconnnected renewable energy generation systems[J].Automation of Electric Power Systems,2017,41(12):26-32(in Chinese).
[26]黄彦浩,于之虹,谢昶,等.电力大数据技术与电力系统仿真计算结合问题研究[J].中国电机工程学报,2015,35(1):13-22.Huang Yanhao,Yu Zhihong,Xie Chang,et al.Study on the application of electric power big data technology in power system simulation[J].Proceedings of the CSEE,2015,35(1):13-22(in Chinese).
[27]汤涌.基于响应的电力系统广域安全稳定控制[J].中国电机工程学报,2014,34(29):5041-5050.Tang Yong.Response-based wide area control for power system security and stability[J].Proceedings of the CSEE,2014,34(29):5041-5050(in Chinese).
[28]刘强,石立宝,周明,等.现代电力系统恢复控制研究综述[J].电力自动化设备,2007(11):104-110.Liu Qiang,Shi Libao,Zhou Ming,et al.Survey of power system restoration control[J].Electric Power Automation Equipment,2007(11):104-110(in Chinese).
[29]汪震,宋晓喆,杨正清,等.考虑暂态安全的预防-紧急协调控制问题研究[J].中国电机工程学报,2014,34(34):6159-6166.Wang Zheng,Song Xiaozhe,Yang Zhengqing,et al.Acoordinated preventive and emergency control considering system transient security[J].Proceedings of the CSEE,2014,34(34):6159-6166(in Chinese).
[30]兰洲,倪以信,甘德强.现代电力系统暂态稳定控制研究综述[J].电网技术,2005,29(15):40-50.Lan Zhou,Ni Yixin,Gan Deqiang.A survey on transient stability control of modern power systems[J].Power System Technology,2005,29(15):40-50(in Chinese).
[31]许洪强,姚建国,南贵林,等.未来电网调度控制系统应用功能的新特征[J].电力系统自动化,2018,42(1):1-7.Xu Hongqiang,Yao Jianguo,Nan Guilin,et al.New features of application function for future dispatching and control systems[J].Automation of Electric Power System,2018,42(1):1-7(in Chinese).
[32]童晓阳,叶圣永.数据挖掘在电力系统暂态稳定评估中的应用综述[J].电网技术,2009,33(20):88-93.Tong Xiaoyang,Ye Shengyong.A survey on application of data mining in transient stability assessment of power system[J].Power System Technology,2009,33(20):88-93(in Chinese).
[33]姚德全,贾宏杰,赵帅.基于复合神经网络的电力系统暂态稳定评估和裕度预测[J].电力系统自动化,2013,37(20):41-46.Yao Dequan,Jia Hongjie,Zhao Shuai.Power system transient stability assessment and stability margin prediction based on compound neural network[J].Automation of Electric Power Systems,2013,37(20):41-46(in Chinese).
[34]田芳,周孝信,于之虹.基于支持向量机综合分类模型和关键样本集的电力系统暂态稳定评估[J].电力系统保护与控制,2017,45(22):1-8.Tian Fang,Zhou Xiaoxin,Yu Zhihong.Power system transient stability assessment based on comprehensive SVM classification model and key sample set[J].Power System Protection and Control,2017,45(22):1-8(in Chinese).
[35]Gomez F R,Rajapakse A D,Annakkage U D,et al.Support vector machine-based algorithm for post-fault transient stability status prediction using synchronized measurements[J].IEEE Transactions on Power Systems,2011,26(3):1474-1483.
[36]周艳真,吴俊勇,冀鲁豫,等.基于两阶段支持向量机的电力系统暂态稳定预测及预防控制[J].中国电机工程学报,2018,38(1):137-147.Zhou Yanzhen,Wu Junyong,Ji Luyu,et al.Two-stage support vector machines for transient stability prediction and preventive control of power systems[J].Proceedings of the CSEE,2018,38(1):137-147(in Chinese).
[37]Guo T,Milanovic J V.Probabilistic framework for assessing the accuracy of data mining tool for online prediction of transient stability[J].IEEE Transactions on Power Systems,2013,29(1):377-385.
[38]李兆伟,吴雪莲,庄侃沁,等.“9·19”锦苏直流双极闭锁事故华东电网频率特性分析及思考[J].电力系统自动化,2017,41(7):149-155.Li Zhaowei,Wu Xuelian,Zhuang Kanqin,et al.Analysis and reflection on frequency characteristics of east China grid after bipolar locking of“9·19”Jinping-Sunan DCtransmission line[J].Automation of Electric Power Systems,2017,41(7):149-155(in Chinese).
[39]汤奕,王琦,倪明,等.电力和信息通信系统混合仿真方法综述[J].电力系统自动化,2015,39(23):33-42.Tang Yi,Wang Qi,Ni Ming,et al.Review on the hybrid simulation method for power and communication system[J].Automation of Electric Power Systems,2015,39(23):33-42(in Chinese).
[40]Tang Y,Cui H,Wang Q.Prediction model of the power system frequency using a cross-entropy ensemble algorithm[J].Entropy,2017,19(10):552.
[41]Zhang Y,Xu Y,Dong Z Y,et al.Intelligent early warning of power system dynamic insecurity risk:toward optimal accuracy-earliness tradeoff[J].IEEE Transactions on Industrial Informatics,2017,13(5):2544-2554.
[42]Zheng C,Malbasa V,Kezunovic M.Regression tree for stability margin prediction using synchrophasor measurements[J].IEEE Transactions on Power Systems,2013,28(2):1978-1987.
[43]管霖,曹绍杰.基于人工智能的大系统分层在线暂态稳定评估[J].电力系统自动化,2000,24(2):22-26.Guan Lin,Tso S K.Combination of heuristic reasoning and ANN to realize on-line transient stability assessment in large scale power systems[J].Automation of Electric Power Systems,2000,24(2):22-26(in Chinese).
[44]朱乔木,党杰,陈金富,等.基于深度置信网络的电力系统暂态稳定评估方法[J].中国电机工程学报,2018,38(3):735-743.Zhu Qiaomu,Dang Jie,Chen Jinfu,et al.A method for power system transient stability assessment based on deep belief networks[J].Proceedings of the CSEE,2018,38(3):735-743(in Chinese).
[45]顾雪平,李扬,吴献吉.基于局部学习机和细菌群体趋药性算法的电力系统暂态稳定评估[J].电工技术学报,2013,28(10):271-279.Gu Xueping,Li Yang,Wu Xianji.Transient stability assessment of power systems based on local learning machine and bacterial colony chemotaxis algorithm[J].Transactions of China Electrotechnical Society,2013,28(10):271-279(in Chinese).
[46]叶圣永,王晓茹,刘志刚,等.基于受扰严重机组特征及机器学习方法的电力系统暂态稳定评估[J].中国电机工程学报,2011,31(1):46-51.Ye Shengyong,Wang Xiaoru,Liu Zhigang,et al.Power system transient stability assessment based on severely distributed generator attributes and machine learning method[J].Proceedings of the CSEE,2011,31(1):46-51(in Chinese).
[47]唐飞,王波,查晓明,等.基于双阶段并行隐马尔科夫模型的电力系统暂态稳定评估[J].中国电机工程学报,2013,33(10):90-97+14.Tang Fei,Wang Bo,Zha Xiaoming,et al.Power system transient stability assessment based on two-stage parallel hidden Markov model[J].Proceedings of the CSEE,2013,33(10):90-97+14(in Chinese).
[48]Jieping Ye,Ravi Janardan,Qi Li,et al.Feature reduction via generalized uncorrelated linear discriminant analysis[J].IEEE Transactions on Knowledge and Data Engineering,2006,18(10):1312-1322.
[49]顾雪平,曹绍杰,张文勤.人工神经网络和短时仿真结合的暂态安全评估事故筛选方法[J].电力系统自动化,1999,23(8):16-19.Gu Xueping,S K Tso,Zhang Wenqing.Integration of ANNs and short-duration numerical simulation for contingency screening of transient security assessment[J].Automation of Electric Power Systems,1999,23(8):16-19(in Chinese).
[50]刘艳,顾雪平,李军.用于暂态稳定评估的人工神经网络输入特征离散化方法[J].中国电机工程学报,2005,25(15):56-61.Liu Yan,Gu Xueping,Li Jun.Discretization in artificial neural networks used for transient stability assessment[J].Proceedings of the CSEE,2005,25(15):56-61(in Chinese).
[51]Amjady N,Majedi S F.Transient stability prediction by a hybrid intelligent system[J].IEEE Transactions on Power Systems,2007,22(3):1275-1283.
[52]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(in Chinese).
[53]叶圣永,王晓茹,刘志刚,等.基于支持向量机增量学习的电力系统暂态稳定评估[J].电力系统自动化,2011,35(11):15-19.Ye Shengyong,Wang Xiaoru,Liu Zhigang,et al.Power system transient stability assessment based on support vector machine incremental learning method[J].Automation of Electric Power Systems,2011,35(11):15-19(in Chinese).
[54]He M,Zhang J,Vittal V.Robust online dynamic security assessment using adaptive ensemble decision-tree learning[J].IEEE Transactions on Power Systems,2013,28(4):4089-4098.
[55]卢锦玲,朱永利,赵洪山,等.提升型贝叶斯分类器在电力系统暂态稳定评估中的应用[J].电工技术学报,2009,24(5):177-182.Lu Jinling,Zhu Yongli,Zhao Hongshan,et al.Power system transient stability assessment based on boosting Bayesian classifier[J].Transactions of China Electrotechnical Society,2009,24(5):177-182(in Chinese).
[56]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).
[57]胡伟,郑乐,闵勇,等.基于深度学习的电力系统故障后暂态稳定评估研究[J].电网技术,2017,41(10):3140-3146(in Chinese).Hu Wei,Zheng Le,Min Yong,et al.Research on power system transient stability assessment based on deep learning of big data technique[J].Power System Technology,2017,41(10):3140-3146(in Chinese).
[58]Hadidi R,Jeyasurya B.Reinforcement learning based real-time wide-area stabilizing control agents to enhance power system stability[J].IEEE Transactions on Smart Grid,2013,4(1):489-497.
[59]段青,赵建国,马艳.基于稀疏贝叶斯学习的电力系统暂态稳定评估[J].电力自动化设备,2009,29(9):36-40.Duan Qing,Zhao Jianguo,Ma Yan.Power systems transient stability assessment based on sparse Bayesian learning[J].Electric Power Automation Equipment,2009,29(9):36-40(in Chinese).
[60]Amraee T,Ranjbar S.Transient instability prediction using decision tree technique[J].IEEE Transactions on Power Systems,2013,28(3):3028-3037.
[61]Zheng C,Malbasa V,Kezunovic M.Regression tree for stability margin prediction using synchrophasor measurements[J].IEEE Transactions on Power Systems,2013,28(2):1978-1987.
[62]Goodfellow I J,Pouget-Abadie J,Mirza M,et al.Generative adversarial nets[J].Advances in Neural Information Processing Systems,2014:2672-2680.
[63]Cutler M,Walsh T J,How J P.Real-world reinforcement learning via multifidelity simulators[J].IEEE Transactions on Robotics,2017,31(3):655-671.
[64]Jouppi N P,Young C,Patil N,et al.In-datacenter performance analysis of a tensor processing unit[C]//Proceedings of the 44th Annual International Symposium on Computer Architecture.Toronto,Canada,2017.
[65]薛禹胜.因果分析及机器学习之间的壁垒与融合[R].北京:中国电力科学研究院,北京,2016.Xue Yusheng.Barrier and integration between causal analysis and machine learning[R].Beijing:China Electric Power Research Institute,2016(in Chinese).
[66]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+5833.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+5833(in Chinese).
[67]Wang J,Zhong H,Lai X,et al.Exploring key weather factors from analytical modeling toward improved solar power forecasting[J].IEEE Transactions on Smart Grid,2017,PP(99):1-1.
[68]王琦,李峰,汤奕,等.基于物理-数据融合模型的电网暂态频率特征在线预测方法[J].电力系统自动化,2018,42(19):1-9.Wang Qi,Li Feng,Tang Yi,et al.Automation of Electric Power Systems,2018,42(19):1-9(in Chinese).
[69]董娜,刘伟娜,侯波涛.基于大数据的网络异常行为建模方法[J].电力信息与通信技术,2018,16(1):6-10.Dong Na,Liu Weina,Hou Botao.Modeling method of network abnormal behavior based on big data[J].Electric Power Information and Communication Technology,2018,16(1):6-10(in Chinese).