Prediction of power network stability based on an adaptive neural network

ZHAO Bo; TIAN Xiu-xia; LI Can

doi:10.3969/j.issn.1000-5641.2019.05.011

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Dec. 2019

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ZHAO Bo, TIAN Xiu-xia, LI Can. Prediction of power network stability based on an adaptive neural network[J]. Journal of East China Normal University (Natural Sciences), 2019, (5): 133-142. doi: 10.3969/j.issn.1000-5641.2019.05.011

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ZHAO Bo, TIAN Xiu-xia, LI Can. Prediction of power network stability based on an adaptive neural network[J]. Journal of East China Normal University (Natural Sciences), 2019, (5): 133-142. doi: 10.3969/j.issn.1000-5641.2019.05.011

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Prediction of power network stability based on an adaptive neural network

doi: 10.3969/j.issn.1000-5641.2019.05.011

College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 200090, China

Received Date: 2019-07-27
Publish Date: 2019-09-25

Abstract

Abstract

The safety and stability of the power grid serves as the basis for reform, development, and stability of power enterprises as well as for broader society. With the increasing complexity of power grid structures, safety and stability of the power grid is important for ensuring the rapid and effective development of the national economy. In this paper, we propose an optimal neural network stability prediction model and compare performance with classical machine learning methods. By analyzing the UCI2018 grid stability simulation dataset, the experimental results show that the proposed method can achieve higher prediction accuracy and provide a new approach for research of power big data.
- grid stability,
- support vector machine,
- decision tree,
- neural network

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References(23)

References

[1]	赵一安.电气工程及其自动化技术下的电力系统自动化发展研究[J].黑龙江科学, 2018, 9(5):76-77. doi: 10.3969/j.issn.1674-8646.2018.05.035
[2]	和萍, 文福拴, 薛禹胜, 等.风电场并网对互联系统小干扰稳定及低频振荡特性的影响[J].电力系统自动化, 2014, 38(22):1-10. doi: 10.7500/AEPS20140613003
[3]	卫思明, 黄永章.新能源采用MGP并网的小干扰稳定分析[J].华北电力大学学报(自然科学版), 2018, 45(1):22-30. doi: 10.3969/j.ISSN.1007-2691.2018.01.04
[4]	DAOWEI L, DUNWEN S, HONGYIN W, et al. Voltage stability online evaluation system based on WAMS and EMS[J]. Power System Technology, 2014, 38(7):1934-1938. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dwjs201407033
[5]	JINDAL A, DUA A, KAUR K, et al. Decision tree and SVM-based data analytics for theft detection in smart grid[J]. IEEE Transactions on Industrial Informatics, 2016, 12(3):1005-1016. doi: 10.1109/TII.2016.2543145
[6]	卢峰, 丁学峰, 尹小明, 等.基于样本优化选取的支持向量机窃电辨识方法[J].计算机测量与控制, 2018, 26(6):223-226. http://d.old.wanfangdata.com.cn/Periodical/jsjzdclykz201806057
[7]	DING N, BENOIT C, FOGGIA G, et al. Neural network-based model design for short-term load forecast in distribution systems[J]. IEEE Transactions on Power Systems, 2015:31(1):1-10. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=abcc9495bd6eb1110c7deb17ce109a88
[8]	ZHENG Z B, YANG Y T, NIU X D, et al. Wide & Deep convolutional neural networks for electricity-theft detection to secure smart grids[J]. IEEE Transactions on Industrial Informatics, 2018, 14(4):1606-1615. doi: 10.1109/TII.2017.2785963
[9]	PIERREVAL H. Rule-based simulation metamodels[J]. European Journal of Operational Research, 1992, 61(1/2):6-17. http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ026348111/
[10]	MCCALLEY J D, WANG S, ZHAO Q L, et al. Security boundary visualization for systems operation[J]. IEEE Transactions on Power Systems, 1997, 12(2):940-947. doi: 10.1109/59.589783
[11]	ROHDEN M, SORGE A, TIMME M, et al. Self-organized synchronization in decentralized power grids[J]. Physical Review Letters, 2012, 109(6):1-5. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=3de4bf5ddc6cd51e72cd0407589bed2e
[12]	FILATRELLA G, NIELSEN A H, PEDERSEN N F. Analysis of a power grid using a Kuramoto-like model[J]. The European Physical Journal B-Condensed Matter, 2008, 61(4):485-491. http://d.old.wanfangdata.com.cn/OAPaper/oai_arXiv.org_0705.1305
[13]	MENCK P J, HEITZIG J, MARWAN N, et al. How basin stability complements the linear-stability paradigm[J]. Nature Physics, 2013, 9(2):89-92. doi: 10.1038/nphys2516
[14]	SCHÄFER B, MATTHIAE M, TIMME M. et al. Corrigendum:Decentral smart grid control[J]. New Journal of Physics, 2015, 17:015002. doi: 10.1088/1367-2630/17/1/015002
[15]	朱利鹏, 陆超, 孙元章, 等.基于数据挖掘的区域暂态电压稳定评估[J].电网技术, 2015, 39(4):1026-1032. http://d.old.wanfangdata.com.cn/Periodical/dwjs201504024
[16]	DEVIE P M, KALYANI S. Transient stability prediction in multimachine system using data mining techniques[J]. Esrsa Publications, 2013(2):2748-2755.
[17]	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. http://d.old.wanfangdata.com.cn/Periodical/dlxtzdh201111003
[18]	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. doi: 10.1109/TPWRS.2010.2082575
[19]	AMRAEE T, RANJBAR S. Transient instability prediction using decision tree technique[J]. IEEE Transactions on Power Systems, 2013, 28(3):3028-3037. doi: 10.1109/TPWRS.2013.2238684
[20]	汤必强, 邓长虹, 刘丽芳.复合神经网络在电力系统暂态稳定评估中的应用[J].电网技术, 2004, 15:62-66. http://d.old.wanfangdata.com.cn/Periodical/dwjs200415014
[21]	ZHOU D Q, ANNAKKAGE U D, RAJAPAKSE A D. Online monitoring of voltage stability margin using an artificial neural network[J]. IEEE Transactions on Power Systems, 2010, 25(3):1566-1574. doi: 10.1109/TPWRS.2009.2038059
[22]	PIERREVAL H, RALAMBONDRAINY H. A simulation and learning technique for generating knowledge about manufacturing systems behavior[J]. Journal of the Operational Research Society, 1990, 41(6):461-474. doi: 10.1057/jors.1990.72
[23]	SCHAFER B, GRABOW C, AUER S, et al. Taming instabilities in power grid networks by decentralized control[J]. The European Physical Journal Special Topics, 2016, 225(3):569-582. doi: 10.1140/epjst/e2015-50136-y