期刊首页 优先出版 当期阅读 过刊浏览 作者中心 关于期刊 English

《工程(英文)》 >> 2020年 第6卷 第7期 doi: 10.1016/j.eng.2020.06.006

智能电网状态估计中用于提高数据完整性的超分辨率感知技术

a School of Science and Engineering, Chinese University of Hong Kong, Shenzhen, Shenzhen 518172, China
b School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China
c Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518172, China
d School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
e School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia

收稿日期: 2019-05-08 修回日期: 2019-10-08 录用日期: 2020-02-19 发布日期: 2020-06-22

下一篇 上一篇

摘要

智能电网是一个结合了可再生能源与先进信息和通信技术的国家关键基础设施,为社会提供经济且安全的电力供应。为了应对可再生能源的间歇性特点并确保智能电网的安全性,应该进行更高频率的状态估计运算,而高频率的状态估计运算需要更加完整的系统状态信息。本文从智能电网状态估计数据完整性角度出发,将如何基于低频数据恢复高频数据的问题视为一个超分辨率感知(super resolution perception, SRP)问题。然后提出了一种新颖的基于机器学习的SRP方法,即超分辨率状态估计网络(super resolution perception net for state estimation, SRPNSE)来提高状态估计的数据完整性。此方法主要包含三部分:特征提取、信息补全和数据重建。算例证明了我们提出的SRPNSE方法在状态估计中从低频数据恢复至高频数据的有效性和实用价值。

图片

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

图11

图12

图13

图14

图15

图16

图17

图18

图19

图20

图21

图22

图23

图24

图25

参考文献

[ 1 ] Abur A, Gomez-Exposito A. Power system state estimation: theory and implementation. New York: Marcel Dekker; 2004. 链接1

[ 2 ] Nagarajan A, Ayyanar R. Design and strategy for the development of energy storage systems in a distribution feeder with penetration of renewable resources. IEEE Trans Sustain Energy 2015;6(3):1085–92. 链接1

[ 3 ] Tan S, De D, Song WZ, Yang J, Das SK. Survey of security advances in smart grid: a data driven approach. IEEE Commun Surv Tutorials 2017;19(1):397–422. 链接1

[ 4 ] Wu FF, Moslehi K, Bose A. Power system control centers: past, present, and future. Proc IEEE 2005;93(11):1890–908. 链接1

[ 5 ] Monticelli A. State estimation in electric power systems: a generalized approach. Boston: Springer; 1999. 链接1

[ 6 ] Yang Q, An D, Min R, Yu W, Yang X, Zhao W. On optimal PMU placement-based defense against data integrity attacks in smart grid. IEEE Trans Inf Forensics Secur 2017;12(7):1735–50. 链接1

[ 7 ] Appasani B, Mohanta DK. A review on synchrophasor communication system: communication technologies, standards and applications. Prot Control Mod Power Syst 2018;3(1):37. 链接1

[ 8 ] Gopakumar P, Mallikajuna B, Reddy MJB, Mohanta DK. Remote monitoring system for real time detection and classification of transmission line faults in a power grid using PMU measurements. Prot Control Mod Power Syst 2018;3 (3):159–68. 链接1

[ 9 ] Fan W, Liao Y. Wide area measurements based fault detection and location method for transmission lines. Prot Control Mod Power Syst 2019;4(4):53–64. 链接1

[10] Liu Y, Ning P, Reiter MK. False data injection attacks against state estimation in electric power grids. ACM Trans Inf Syst Secur 2011;14(1):1–33. 链接1

[11] Liang G, Zhao J, Luo F, Weller SR, Dong Z. A review of false data injection attacks against modern power systems. IEEE Trans Smart Grid 2017;8 (4):1630–8. 链接1

[12] Ashok A, Govindarasu M. Cyber attacks on power system state estimation through topology errors. In: Proceedings of the 2012 IEEE Power and Energy Society General Meeting; 2012 Jul 22–26; San Diego, CA, USA; 2012.

[13] Kim J, Tong L. On topology attack of a smart grid: undetectable attacks and countermeasures. IEEE J Sel Areas Commun 2013;31(7):1294–305. 链接1

[14] Liang G, Weller SR, Zhao J, Luo F, Dong Z. A framework for cyber-topology attacks: line-switching and new attack scenarios. IEEE Trans Smart Grid 2019;10(2):1704–12. 链接1

[15] Li Z, Shahidehpour M, Alabdulwahab A, Abusorrah A. Analyzing locally coordinated cyber–physical attacks for undetectable line outages. IEEE Trans Smart Grid 2018;9(1):35–47. 链接1

[16] Deng R, Zhuang P, Liang H. CCPA: coordinated cyber–physical attacks and countermeasures in smart grid. IEEE Trans Smart Grid 2017;8(5):2420–30. 链接1

[17] Liang G, Weller SR, Zhao J, Luo F, Dong ZY. The 2015 Ukraine blackout: implications for false data injection attacks. IEEE Trans Power Syst 2017;32 (4):3317–8. 链接1

[18] Pietrosemoli L, Rodríguez-Monroy C. The Venezuelan energy crisis: renewable energies in the transition towards sustainability. Renewable Sustainable Energy Rev 2019;105:415–26. 链接1

[19] Ashok A, Govindarasu M, Ajjarapu V. Online detection of stealthy false data injection attacks in power system state estimation. IEEE Trans Smart Grid 2018;9(3):1636–46. 链接1

[20] Esmalifalak M, Liu L, Nguyen N, Zheng R, Han Z. Detecting stealthy false data injection using machine learning in smart grid. IEEE Syst J 2017;11 (3):1644–52. 链接1

[21] Tsai RY, Huang TS. Multipleframe image restoration and registration. In: Huang TS, editor. Advances in computer vision and image processing. Greenwich: JAI Press; 1984. p. 317–39. 链接1

[22] Shinde PD, Nabalwar SL. Image super-resolution. Int J Sci Eng Technol Res 2013;2(7):1505–8. 链接1

[23] Borman S, Stevenson RL. Super-resolution from image sequences—a review. In: Proceedings of the 1998 Midwest Symposium on System and Circuits; 1998 Aug 9–12; Notre Dame, IN, USA; 1998. 链接1

[24] Park SC, Park MK, Kang MG. Super-resolution image reconstruction: a technical overview. IEEE Signal Process Mag 2003;20(3):21–36. 链接1

[25] Dong C, Loy CC, He K, Tang X. Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 2016;38(2):295–307. 链接1

[26] Wang X, Yu K, Wu S, Gu J, Liu Y, Dong C, et al. ESRGAN: enhanced superresolution generative adversarial networks. In: Proceedings of the 2018 European Conference on Computer Vision; 2018 Sep 8–14; Munich, Germany; 2018.

[27] Fan W. Data quality: from theory to practice. ACM SIGMOD Rec 2015;44 (3):7–18. 链接1

[28] Schmid-Velasco M. Key principles of data quality [Internet]. Washington, DC: NAMATI; 2018 Apr [cited 2019 Mar 30]. Available from: https://community.namati.org/t/key-principles-of-data-quality/42841.

[29] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521(7553):436–44. 链接1

[30] Myttenaere AD, Golden B, Le Grand B, Rossi F. Mean absolute percentage error for regression models. Neurocomputing 2016;192:38–48. 链接1

[31] Zhao Z, Li J, Wang L, Wei D, Gao K. Signal-to-noise ratio improvement of MAMR on CoX/Pt media. IEEE Trans Magn 2015;51(11):15303654. 链接1

[32] Ruder S. An overview of gradient descent optimization algorithms. 2017. arXiv:1609.04747v2.

[33] Hinton G, Srivastava N, Swersky K. Lecture 6e: RMSprop: Divide the gradient by a running average of its recent magnitude [Internet]. Coursera Lecture slides; [cited 2019 Apr. 15]. Available from: https://www.cs.toronto.edu/ ~tijmen/csc321/slides/lecture_slides_lec6.pdf.

[34] Rizwan M. Adam optimization algorithm [Internet]. engMRK; 2018 May 20 [cited 2019 Apr 15]. Available from: https://engmrk.com/adam-optimizationalgorithm/.

[35] Introduction of the 9-bus system (WSCC test case) [Internet]. Al-Roomi; 2015 Apr 11 [cited 2018 Nov 9]. Available from: https://www.al-roomi.org/power- flow/9-bus-system.

[36] Gao J, Giri S, Kara EC, Bergés M. PLAID: a public dataset of high-resolution electrical appliance measurements for load identification research: demo abstract. In: Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings; 2014 Nov 4–6; Memphis, TN, USA; 2014. 链接1

[37] Bannore V. Iterative-interpolation super-resolution image reconstruction: a computationally efficient technique. Berlin: Springer-Verlag; 2009. 链接1

相关研究