数字孪生在电力系统应用中的机遇和挑战Opportunities and Challenges of the Digital Twin in Power System Applications
贺兴;艾芊;朱天怡;邱才明;张东霞;
摘要(Abstract):
电力数字孪生系统(digitaltwinofpowersystems,PSDT)旨在充分利用电力物联网所承载的数据流,通过实时态势感知、超实时虚拟推演2种手段来认知电力系统继而辅助电网运管调控的决策制定。不同于现行的仿真软件,PSDT具备数据驱动、实时交互和闭环反馈3大特点。围绕上述2种手段和3大特点,从工程和科学2个视角剖析了PSDT提出的背景和目的,并阐述了其建设的思路和特色,进一步设计了PSDT的实现框架并探究了其建设所面临的关键问题和核心技术。最后,阐明了PSDT在电力系统多个领域的应用现状与前景。文中的工作也是数字孪生(digitaltwin,DT)在电力领域的早期系统性探索,其研究一方面可为电力物联网的建设提供借鉴;另一方面,也会推动DT技术和数据科学在工程中的应用。
关键词(KeyWords): 数字孪生;数据驱动;认知;大数据分析;建模
基金项目(Foundation): 国家自然科学基金项目(51907121)~~
作者(Author): 贺兴;艾芊;朱天怡;邱才明;张东霞;
Email:
DOI: 10.13335/j.1000-3673.pst.2019.1983
参考文献(References):
- [1]郭剑波.电力系统特性演变及相关思考[C]//西安:第二届“清洁能源发展与消纳”专题研讨会,2019-07-20.
- [2] He X,Chu L,Ai Q,et al.Invisible units detection and estimation based on random matrix theory[J].IEEE Transactions on Power System.Accepted.
- [3] Boschert S, Rosen R. Digital twin—the simulation aspect[M].Mechatronic Futures.Springer,Cham,2016:59-74.
- [4] Grieves M.Digital twin:Manufacturing excellence through virtual factory replication[EB/OL].[2019-12-04].http://www.apriso.com/library/Whitepaper_Dr_Grieves_DigitalTwin_Manufacturing Excellence.php.
- [5] Tao F, Zhang H, Liu A, et al. Digital twin in industry:state-of-the-art[J].IEEE Transactions on Industrial Informatics,2018,15(4):2405-2415.
- [6] He X,Chu L,Qiu R C,et al.A novel data-driven situation awareness approach for future grids—using large random matrices for big data modeling[J].IEEE Access,2018,6:13855-13865.
- [7] Gartner. Gartner Top 10 Strategic Technology Trends for 2019[EB/OL].[2019-12-04].https://www.gartner.com/Smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2019/.
- [8] Zhou M,Yan J,Feng D.Digital twin framework and its application to power grid online analysis[J].CSEE Journal of Power and Energy Systems,2019,5(3):391-398.
- [9] Brosinsky C,Westermann D,Krebs R.Recent and prospective developments in power system control centers:Adapting the digital twin technology for application in power system control centers[C]//2018 IEEE International Energy Conference(ENERGYCON).Limassol,2018:1-6.
- [10] Brosinsky C,Song X,Westermann D.Digital twin-concept of a continuously adaptive power system mirror[C]//International ETGCongress;ETG Symposium.VDE,2019:1-6.
- [11] Pileggi P,Verriet J,Broekhuijsen J,et al.A digital twin for cyber-physical energy systems[C]//2019 7th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems(MSCPES).Montreal,Canada,2019:1-6.
- [12] Oederra O,Asensio F,Eguia P,et al.MV cable modeling for application in the digital twin of a windfarm[C]//2019 International Conference on Clean Electrical Power(ICCEP).Otranto,Italy,2019:617-622.
- [13] Muir A,Lopatto J.Final report on the August 14,2003 blackout in the United States and Canada:causes and recommendations[R].US-Canada Power System Outage Task Force,2004.
- [14] Yang B,Yu T,Shu H,et al.Robust sliding-mode control of wind energy conversion systems for optimal power extraction via nonlinear perturbation observers[J].Applied Energy,2018(210):711-723.
- [15] Glaessgen E,Stargel D.The digital twin paradigm for future NASA and US Air Force vehicles[C]//Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference14th AIAA.2012:1818.
- [16] He X,Ai Q,Qiu R C,et al.A big data architecture design for smart grids based on random matrix theory[J].IEEE Transactions on Smart Grid,2015,8(2):674-686.
- [17] Gray J.Jim gray on escience:a transformed scientific method[R].The fourth paradigm:Data-intensive scientific discovery,2009:xvii–xxxi.
- [18] Hong T,Chen C,Huang J,et al.Guest editorial big data analytics for grid modernization[J].IEEE Transactions on Smart Grid,2016,7(5):2395-2396.
- [19] Gao P,Wang M,Ghiocel S G,et al.Missing data recovery by exploiting low-dimensionality in power system synchrophasor measurements[J].IEEE Transactions on Power Systems,2015,31(2):1006-1013.
- [20] He X,Qiu R C,Ai Q,et al.Designing for situation awareness of future power grids:an indicator system based on linear eigenvalue statistics of large random matrices[J].IEEE Access, 2016(4):3557-3568.
- [21] Yan Y,Sheng G,Qiu R C,et al.Big data modeling and analysis for power transmission equipment:a novel random matrix theoretical approach[J].IEEE Access,2017(6):7148-7156.
- [22] Shi X,Qiu R,Ling Z,et al.Spatio-temporal correlation analysis of online monitoring data for anomaly detection and location in distribution networks[J]. IEEE Transactions on Smart Grid,[2019-07-16],DOI:10.1109/tsg.2019.2929219.
- [23] Shi X,Qiu R,Mi T,et al.Adversarial feature learning of online monitoring data for operational risk assessment in distribution networks[J].IEEE Transactions on Power Systems,Accepted,2019.
- [24]徐重酉,韩翊,贺兴,等.基于随机矩阵理论的配电网阵列薄弱性评估系统设计[J].电器与能效管理技术,2018,546(9):61-66.Xu Zhongyou,Han Yi,He Xing,et al.Weak point detection system design based on random matrix theory for distributed network[J].Electrical&Energy Management Technology,2018,546(9):61-66(in Chinese).
- [25]刘威,张东霞,丁玉成,等.基于随机矩阵理论与熵理论的电网薄弱环节辨识方法[J].中国电机工程学报,2017,37(20):5893-5901.Liu Wei, Zhang Dongxia, Ding Yucheng, et al. Power grid vulnerability identification methods based on random matrix theory and entropy theory[J].Proceedings of the CSEE,2017,37(20):5893-5901(in Chinese).
- [26]张力,张子仲,顾建炜.基于随机矩阵理论的电网状态分析与扰动定位方法[J].电力系统自动化,2018,42(12):93-99.Zhang Li,Zhang Zizhong,Gu Jianwei.Three-dimensional SVPWM control strategy for suppressing common-mode voltage of three-phase four-bridge inventer[J].Automation of Electric Power Systems,2018,42(12):93-99(in Chinese).
- [27]刘威,张东霞,王新迎,等.基于随机矩阵理论的电力系统暂态稳定性分析[J].中国电机工程学报,2016,36(18):4854-4863.Liu Wei,Zhang Dongxia,Wang Xinying,et al.Power system transient stability analysis based on random matrix theory[J].Proceedings of the CSEE,2016,36(18):4854-4863(in Chinese).
- [28]吴茜,张东霞,刘道伟,等.基于随机矩阵理论的电网静态稳定态势评估方法[J].中国电机工程学报,2016,36(20):5414-5420.Wu Qian,Zhang Dongxia,Liu Daowei,et al.A method for power system steady stability situation assessment based on random matrix theory[J].Proceedings of the CSEE,2016,36(20):5414-5420(in Chinese).
- [29]张宇航,邱才明,贺兴,等.一种基于LSTM神经网络的短期用电负荷预测方法[J].电力信息与通信技术,2017,15(9):19-25.Zhang Yuhang,Qiu Caiming,He Xing,et al.A short-term load forecasting based on LSTM neural network[J].Power Information and Communication Technology,2017,15(9):19-25(in Chinese).
- [30] Kelly J,Knottenbelt W.Neural nilm:Deep neural networks applied to energy disaggregation[C]//Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments.ACM,2015:55-64.
- [31]徐舒玮,邱才明,张东霞,等.基于深度学习的输电线路故障类型辨识[J].中国电机工程学报,2019,39(1):65-74.Xu Shuwei,Qiu Caiming,Zhang Dongxia,et al.A deep learning approach for fault type identification of transmission line[J].Proceedings of the CSEE,2019,39(1):65-74(in Chinese).
- [32]陈伟彪,陈亦平,姚伟,等.基于随机矩阵理论的故障时刻确定和故障区域定位方法[J].中国电机工程学报,2018,38(6):1655-1664.Chen Weibiao,Chen Yiping,Yao Wei,et al.A random matrix theory-based approach to fault time determination and fault area location[J].Proceedings of the CSEE,2018,38(6):1655-1664(in Chinese).
- [33]张自东,邱才明,张东霞,等.基于深度强化学习的微电网复合储能协调控制方法[J].电网技术,2019,43(6):1914-1921.Zhang Zidong,Qiu Caiming,Zhang Dongxia,et al.A coordinated control method for hybrid energy storage system in microgrid based on deep reinforcement learning[J].Power System Technology,2019,43(6):1914-1921(in Chinese).
- [34] Yang B,Zhong L,Zhang X,et al.Novel bio-inspired memetic salp swarm algorithm and application to MPPT for PV systems considering partial shading condition[J].Journal of Cleaner Production,2019,215:1203-1222.
- [35] Yang B,Yu T,Zhang X,et al.Dynamic leader based collective intelligence for maximum power point tracking of PV systems affected by partial shading condition[J].Energy Conversion and Management,2019(179):286-303.
- [36] Ling Z,Qiu R C,Jin Z,et al.An accurate and real-time self-blast glass insulator location method based on faster R-CNN and U-net with aerial images[J].arXiv preprint arXiv:1801.05143.
- [37]张宇航,邱才明,贺兴,等.深度学习在电网图像数据及时空数据中的应用综述[J].电网技术,2019,43(6):1865-1873.Zhang Yuhang,Qiu Caiming,He Xing,et al.Overview of application of deep learning with image data and spatio-temporal data of power grid[J].Power System Technology,2019,43(6):1865-1873(in Chinese).
- [38] Burges C,Shaked T,Renshaw E,et al.Learning to rank using gradient descent[C]//Proceedings of the 22nd International Conference on Machine learning(ICML-05).2005:89-96.
- [39] Yuan Y,Ardakanian O,Low S,et al.On the inverse power flow problem[J].arXiv preprint arXiv:1610.06631,2016.
- [40] Chen Y C,Wang J,Domínguez-García A D,et al.Measurement-based estimation of the power flow Jacobian matrix[J].IEEE Transactions on Smart Grid,2015,7(5):2507-2515.
- [41] Zimmerman R D, Murillo-Sánchez C E. Matpower 4.1 user’s manual[Z].Power Systems Engineering Research Center,Cornell University,Ithaca,NY,2011.
- [42] Vaccaro A,Ca?izares C A.A knowledge-based framework for power flow and optimal power flow analyses[J].IEEE Transactions on Smart Grid,2016,9(1):230-239.
- [43] Li P,Su H,Wang C,et al.PMU-based estimation of voltage-to-power sensitivity for distribution networks considering the sparsity of Jacobian matrix[J].IEEE Access,2018(6):31307-31316.
- [44] Wu F F,Liu W H E.Detection of topology errors by state estimation(power systems)[J].IEEE Transactions on Power Systems,1989,4(1):176-183.
- [45]章健,艾芊,王新刚.多代理系统在微电网中的应用[J].电力系统自动化,2008,32(24):80-82.Zhang Jian,Ai Qian,Wang Xingang.Application of multi-agent system in microgrid[J].Automation of Electric Power Systems,2008,32(24):80-82(in Chinese).
- [46]范松丽,艾芊,贺兴.基于机会约束规划的虚拟电厂调度风险分析[J].中国电机工程学报,2015,35(16):4025-4034.Fan Songli,Ai Qian,He Xing.Risk analysis on dispatch of virtual power plant based on chance constrained progr amming[J].Proceedings of the CSEE,2015,35(16):4025-4034(in Chinese).