基于随机矩阵理论的电网薄弱环节辨识策略

doi:10.16180/j.cnki.issn1007-7820.2024.07.001

摘要/Abstract

摘要：

随着电网规模不断扩大,新型电力系统的运行特性愈加复杂,准确辨识电网存在的薄弱环节对提高系统的监测能力和保障系统的可靠运行具有重要的实际意义。文中基于随机矩阵理论提出了一种电网薄弱环节辨识策略。该策略利用电网运行中的实测数据构造随机矩阵,基于随机矩阵理论构建薄弱环节的判断指标,并设计了薄弱节点和薄弱支路的判断方法。该方法从数据相关性的角度进行分析,不需要考虑实际电网复杂的网络结构和运行机理,因此没有复杂的建模过程。为了验证该方法的可行性,通过IEEE 39节点系统搭建了算例仿真,并与传统方法进行了对比。结果表明,所提策略在辨识薄弱节点和支路上具有较好效果,且准确性相较于传统方法有所提升。

关键词: 大电网, 新型电力系统, 实测数据, 薄弱环节辨识, 薄弱节点, 薄弱支路, 数据驱动, 随机矩阵理论

Abstract:

With the continuous expansion of the scale of bulk power grid, the operating characteristics of new power system has become more and more complex. Accurately identifying the weak links in the power grid is of great practical significance for improving the monitoring ability of the system and ensuring its reliable operation. Therefore, an identification strategy of weak links for power grid is proposed based on random matrix theory. The strategy uses the measured data in power grid operation to construct a random matrix and uses the weak links judgment index based on the random matrix theory to design the judgment method of weak nodes and weak branches. This method is analyzed from the perspective of data correlation, without considering the complex network structure and operation mechanism of the actual power grid, so there is no complex modeling process. To verify the feasibility of the method, an example simulation is established through the IEEE 39 node system and compared with the traditional methods. The results show that the proposed identification strategy has a good effect on identifying weak nodes and branches, and the accuracy is improved compared with the traditional methods.

Key words: bulk power grid, new power system, measured data, weak link identification, weak nodes, weak branch, data-driven, random matrix theory

中图分类号:

TP391

阳杰, 孙伟卿, 马美玲. 基于随机矩阵理论的电网薄弱环节辨识策略[J]. 电子科技, 2024, 37(7): 1-8.

YANG Jie, SUN Weiqing, MA Meiling. Identification Strategy of Power Grid Weak Links Based on Random Matrix Theory[J]. Electronic Science and Technology, 2024, 37(7): 1-8.

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参考文献 22

[1]	文劲宇, 周博, 魏利屾. 中国未来电力系统储电网初探[J]. 电力系统保护与控制, 2022, 50(7):1-10.
	Wen Jinyu, Zhou Bo, Wei Lishen. Preliminary study on an energy storage grid for future power system in China[J]. Power System Protection and Control, 2022, 50(7):1-10.
[2]	谷峰. 新型电力系统到底“新”在哪里?[J]. 中国电力企业管理, 2022(10):42-45.
	Gu Feng. Where is the new power system "new"?[J]. China Power Enterprise Management, 2022(10):42-45.
[3]	满延露, 刘敏, 王锴. 主动配电网态势感知技术研究综述与展望[J]. 电子科技, 2014, 37(2):6-13.
	Man Yanlu, Liu Min, Wang Kai. A review and prospect of research on situational awareness technology in active distribution network[J]. Electronic Science and Technologly, 2014, 37(2):6-13.
[4]	栾翔, 于群, 贺庆, 等. 考虑一致性风险价值的电网薄弱环节综合评估指标[J]. 中国电力, 2017, 50(6):62-68.
	Luan Xiang, Yu Qun, He Qing, et al. Integrated assessment index of power grid's weak links by considering coherent value at risk[J]. Electric Power, 2017, 50(6):62-68.
[5]	李玲玲, 鲁修学, 孙东旺, 等. 基于元件功率的区域电力系统薄弱环节辨识算法[J]. 电力系统保护与控制, 2014, 42(4):77-83.
	Li Lingling, Lu Xiuxue, Sun Dongwang, et al. An identification algorithm of the weak links for regional power system based on element power[J]. Power System Protection and Control, 2014, 42(4):77-83.
[6]	Nasiruzzaman A B M, Akter M N, Mahmud M A, et al. Network theory based power grid criticality assessment[C]. Chennai: IEEE International Conference on Power Electronics,Drives and Energy Systems, 2018:681-685.
[7]	李勇, 刘俊勇, 刘晓宇, 等. 基于潮流熵的电网连锁故障传播元件的脆弱性评估[J]. 电力系统自动化, 2012, 36(19):11-16.
	Li Yong, Liu Junyong, Liu Xiaoyu, et al. Vulnerability assessment in power grid cascading failures based on entropy of power flow[J]. Automation of Electric Power Systems, 2012, 36(19):11-16.
[8]	王涛, 岳贤龙, 顾雪平, 等. 基于奇异值熵和潮流分布熵的电网关键节点辨识[J]. 电力自动化设备, 2016, 36(4):46-53.
	Wang Tao, Yue Xianlong, Gu Xueping, et al. Power grid critical node identification based on singular value entropy and power flow distribution entropy[J]. Electric Power Automation Equipment, 2016, 36(4):46-53.
[9]	谢李为, 李勇, 罗隆福, 等. 基于复杂网络与运行因素的电网薄弱点辨识方法[J]. 电力系统保护与控制, 2022, 50(4):83-91.
	Xie Liwei, Li Yong, Luo Longfu, et al. A vulnerable points identification method based on complex network theory and an operation index[J]. Power System Protection and Control, 2022, 50(4):83-91.
[10]	郭琦, 卢远宏. 新型电力系统的建模仿真关键技术及展望[J]. 电力系统自动化, 2022, 46(10):18-32.
	Guo Qi, Lu Yuanhong. Key technologies and prospects of modeling and simulation of new power system[J]. Automation of Electric Power Systems, 2022, 46(10):18-32.
[11]	李鹏, 习伟, 蔡田田, 等. 数字电网的理念、架构与关键技术[J]. 中国电机工程学报, 2022, 42(14):5002-5017.
	Li Peng, Xi Wei, Cai Tiantian, et al. Concept,architecture and key technologies of digital power grids[J]. Proceedings of the CSEE, 2022, 42(14):5002-5017.
[12]	Couillet R, Debbah M. Random matrix methods for wireless communications[M]. Cambridge: Cambridge University Press, 2011:114-118.
[13]	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):5414-5420,5717.
	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):5414-5420,5717.
[14]	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):4854-4863,5109.
	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):4854-4863,5109.
[15]	Xu X, He X, Ai Q, et al. A correlation analysis method for power systems based on random matrix theory[J]. IEEE Transactions on Smart Grid, 2017(4):1811-1820.
[16]	束洪春, 王广雪, 田鑫萃, 等. 基于随机矩阵理论的混合三端直流线路非单元式纵联保护[J]. 电力系统自动化, 2022, 46(19):162-171.
	Shu Hongchun, Wang Guangxue, Tian Xincui, et al. Non-unit pilot protection for hybrid three-terminal DC lines based on random matrix theory[J]. Automation of Electric Power Systems, 2022, 46(19):162-171.
[17]	Laloux L, Cizeau P, Potters M, et al. Random matrix theory and financial correlations[J]. International Journal of Theoretical and Applied Finance, 2000, 3(3):391-397.
[18]	郭拓, 王英民, 张立琛. 采用随机矩阵理论的水声阵列SMI-MVDR空间谱估计技术[J]. 火力与指挥控制, 2017, 42(3):45-48,54.
	Guo Tuo, Wang Yingmin, Zhang Lichen. Underwater acoustic array SMI-MVDR spatial spectral estimation based on random matrix theory[J]. Fire Control and Command Control, 2017, 42(3):45-48,54.
[19]	刘铭, 徐丽, 王晓懿, 等. 基于随机矩阵理论的大规模MIMO系统窃听用户检测[J]. 北京交通大学学报, 2020, 44(2):58-65. doi: 10.11860/j.issn.1673-0291.20190049
	Liu Ming, Xu Li, Wang Xiaoyi, et al. Active eavesdropper detection based on large dimension random matrix theory for massive MIMO systems[J]. Journal of Beijing Jiaotong University, 2020, 44(2):58-65. doi: 10.11860/j.issn.1673-0291.20190049
[20]	程含渺, 李红斌, 徐晴, 等. 基于高维随机矩阵的系统状态评估方法研究[J]. 电力工程技术, 2018, 37(1):72-78.
	Cheng Hanmiao, Li Hongbin, Xu Qing, et al. Research on system condition assessment based on large dimensional random matrices[J]. Electric Power Engineering Technology, 2018, 37(1):72-78.
[21]	王涛, 高成彬, 顾雪平, 等. 基于功率介数的电网关键环节辨识[J]. 电网技术, 2014, 38(7):1907-1913.
	Wang Tao, Gao Chengbin, Gu Xueping, et al. Power betweenness based identification of power grid critical links[J]. Power System Technology, 2014, 38(7):1907-1913.
[22]	鞠文云, 李银红. 基于最大流传输贡献度的电力网关键线路和节点辨识[J]. 电力系统自动化, 2012, 36(9):6-12.
	Jun Wenyun, Li Yinhong. ldentification of critical lines and nodes in power grid based on maximum flow transmission contribution degree[J]. Automation of Electric Power Systems, 2012, 36(9):6-12.

方法	α值
综合评估指标	15.668
潮流熵	5.441
功率介数	4.902
本文方法	34.775

序号	复杂网络理论^[22]	潮流分布熵法^[8]	本文方法
1	15-16	5-6	16-19
2	16-17	16-19	6-11
3	14-15	19-20	10-13
4	16-19	6-7	16-21
5	2-3	6-11	3-18
6	13-14	15-16	15-16
7	16-24	4-5	16-17
8	2-25	2-25	4-14
9	4-5	13-14	4-5
10	17-18	16-17	13-14

方法	α值
复杂网络理论	3.018
潮流分布熵	6.661
本文方法	14.001