复杂电网自组织临界态辨识物理指标研究

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摘要

在深入研究电网自组织临界特性的基础上,指出网络结构和潮流分布的高度不均衡性是电网进入自组织临界态的两个重要因素。对于网络分布的均衡与否可采用网络拓扑熵刻画;潮流分布的不均衡性可分为由于部分线路负载率偏低而致和由于部分线路负载率偏高而致两类,显然后者才是电网进入自组织临界态的真正因素之一。由于潮流熵概念无法区分这两类完全不同的潮流分布特性,本文提出了一个新的加权潮流熵概念来表征潮流分布的不均衡性。通过对实际电网的仿真分析,表明电网处于自组织临界态时,呈现低网络拓扑熵和高加权潮流熵特性;加权潮流熵与潮流熵相比,能够更好地识别出由于部分线路负载率偏高而致的潮流不均衡性这一关键因素。网络拓扑熵和加权潮流熵两个物理指标的提出能够为复杂电网自组织临界态的辨识与综合评估以及连锁故障的预防控制提供一定的理论支撑。

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	刘文颖
	但扬清
	朱艳伟
	梁才
	蔡万通
	王维洲
	郑伟

关键词 ：连锁故障, 自组织临界态, 网络拓扑熵, 加权潮流熵, 潮流熵

Abstract：

After self-organized criticality described in detail, it is pointed out that high inhomogeneity of network and power flow distribution are important factors to enter self-organized critical state. Network topology entropy can be used to describe the inhomogeneity of the network distribution. The inhomogeneity of the power flow distribution is divided into two kinds caused by some transmission line with low load rate and by some transmission line with high load rate respectively; then it is put forward that the latter is the real factors of the grid to enter one of the self-organized critical state. As power flow entropy can not completely distinguish two different power flow distribution characteristics, the weighted power flow entropy is proposed to feature inhomogeneity of power flow distribution. Case study shows when the grid is in a self-organized critical state, the grid features low network topology entropy and high weighted power flow entropy. The weighted power flow entropy is better able to identify inhomogeneity of power flow distribution caused by some transmission line with high load rate compared with power flow entropy. Network topology entropy and weighted power flow entropy are used for self-organized critical state identification, assessment and cascading failures prevention control.

Key words： Cascading failure self-organized critical state network topology entropy weighted power flow entropy power flow entropy

收稿日期: 2013-07-05 出版日期: 2014-11-05

PACS:

TM732

作者简介: 刘文颖女,1955年生,教授,博士生导师,研究方向为电力系统分析与控制及电力系统智能调度；但扬清男,1983年生,博士研究生,研究方向为电力系统分析与控制。

引用本文:

刘文颖, 但扬清, 朱艳伟, 梁才, 蔡万通, 王维洲, 郑伟. 复杂电网自组织临界态辨识物理指标研究[J]. 电工技术学报, 2014, 29(8): 274-280,288. Liu Wenying, Dan Yangqing, Zhu Yanwei, Liang Cai, Cai Wantong, Wang Weizhou, Zheng Wei. Research on Physical Indicators to Identify Power System Self-Organized Critical State. Transactions of China Electrotechnical Society, 2014, 29(8): 274-280,288.

链接本文:

http://dgjsxb.ces-transaction.com/CN/Y2014/V29/I8/274

[1] Carreras B A, Newman D E, Dobson I, et al. Evidence forself-organized criticality in electric power system blackouts[C]. Hawaii International Conference on System Sciences, Hawaii, 2001: 705-709.
[2] 曹一家, 江全元, 丁理杰. 电力系统大停电的自组织临界现象[J]. 电网技术, 2005, 29(15): 1-5. Cao Yijia, Jiang Quanyuan, Ding Lijie. Self-organized criticality phenomenon for power system blackouts[J]. Power System Technology, 2005, 29(15): 1-5.
[3] 于群, 郭剑波. 中国电网停电事故统计与自组织临界特性特征[J]. 电力系统自动化, 2006, 30(2): 16-21. Yu Qun, Guo Jianbo. Statistics and self-organized criticality characters of blackouts in China electric power systems[J]. Automation of Electric Power Systems, 2006, 30(2): 16-21.
[4] Dobson I, Carreras B A, Lynch V E, et al. An initial model for complex dynamics in electric power system blackouts[C]. Proceedings of the 34th Hawaii International Conference on System Sciences, Maui, Hawaii, 2001, 2: 710-718.
[5] 郑阳, 刘文颖, 温志伟, 等. 基于小世界网络的电网连锁故障实时搜索系统[J]. 电网技术, 2010, 34(7): 58-63. Zheng Yang, Liu Wenying, Wen Zhiwei, et al. A real-time searching system for cascading failures of power grids based on small-world network[J]. Power System Technology, 2010, 34(7): 58-63.
[6] 王英英, 罗毅, 涂光瑜, 等. 电力系统连锁故障的关联模型[J]. 电工技术学报, 2012, 27(2): 204-209. Wang Yingying, Luo Yi, Tu Guangyu. Correlation model of cascading failures in power system[J]. Transaction of China Eletrotechnical Society, 2012, 27(2): 204-209.
[7] 易俊, 周孝信. 考虑系统频率特性以及保护隐藏故障的电网连锁故障模型[J]. 电力系统自动化, 2006, 30(14): 1-5. Yi Jun, Zhou Xiaoxin. A cascading failure model of power system considering frequency response characteristics and hidden failures[J]. Automation of Electric Power Systems, 2006, 30(14): 1-5.
[8] 曹一家, 丁理杰, 等, 考虑网络演化的直流潮流停电模型与自组织临界特性分析[J]. 电力系统自动化, 2009, 33(5): 1-6. Cao Yijia, Ding Lijie, et al. Analysis on cascading failure and self-organized criticality in evolving power grids[J]. Automation of Electric Power Systems, 2009, 33(5): 1-6.
[9] 梅生伟, 何飞, 张雪敏, 等. 一种改进的OPA模型及大停电风险评估[J]. 电力系统自动化, 2008, 32(13): 1-5. Mei Shengwei, He Fei, Zhang Xuemin, et al. An improved OPA model and the evaluation of blackout risk[J]. Automation of Electric Power Systems, 2008, 32(13): 1-5.
[10] 周孝信, 肖逾男. 用连锁故障搜索算法判别系统的自组织临界状态[J]. 中国电机工程学报, 2007, 27(25): 1-5. Zhou Xiaoxin, Xiao Yunan. Determining the self- organized criticality state of power systems by the cascading failures searching method[J]. Proceedings of the CSEE, 2007, 27(25): 1-5.
[11] 梁才, 刘文颖, 温志伟, 等. 电网组织结构对其自组织临界特性的影响[J]. 电力系统保护与控制, 2010, 38(20): 6-11. Liang Cai, Liu Wenying, Wen Zhiwei, et al. The influences of power grid structure on self-organized criticality[J]. Power System Protection and Control, 2010, 38(20): 6-11.
[12] 曹一家, 王光增, 曹丽华, 等, 基于潮流熵的复杂电网自组织临界态判断模型[J]. 电力系统自动化, 2011, 35(7): 1-6. Cao Yijia, Wang Guangzeng, Cao Lihua, et al. An identification model for self-organized criticality of power grids based power flow entropy[J]. Automation of Electric Power Systems, 2011, 35(7): 1-6.
[13] 于群, 曹娜, 郭剑波. 负载率对电力系统自组织临界状态的影响分析[J]. 电力系统自动化, 2012, 36(1): 24-27, 37. Yu Qun, Cao Na, Guo Jianbo. Analysis on influence of load rate on power system self-orgnized criticality [J]. Automation of Electric Power Systems, 2012, 36(1): 24-27, 37.
[14] 陈晓刚, 孙可, 曹一家. 基于复杂网络理论的大电网结构脆弱性分析[J]. 电工技术学报, 2007, 22(10): 138-144. Chen Xiaogang, Sun Ke, Cao Yijia. Structural vulnerability analysis of large power grid based on complex network theory[J]. Transaction of China Eletrotechnical Society, 2007, 22(10): 138-144.
[15] 曹一家, 陈彦如, 曹丽华, 等. 复杂系统理论在电力系统中的应用研究展望[J]. 中国电机工程学报, 2012, 32(19): 1-9. Cao Yijia, Chen Yanru, Cao Lihua, et al. Prospects of studies on application of complex system theory in power systems[J]. Proceedings of the CSEE, 2012, 32(19): 1-9.
[16] Wang J W, Rong L L. Cascade-based attack vulnerability on the US power grid[J]. Safety Science, 2009, 47(10): l332-l336.
[17] 杨建民, 张宁. 复杂网络演化的自组织现象[J]. 上海理工大学学报, 2005, 27(5): 413-416. Yang Jianmin, Zhang Ning. Self-organization pheno- mena of complex network evolution[J]. Journal of University of Shanghai for Science and Technology, 2005, 27(5): 413-416.
[18] Bao Z J, Cao Y J, Wang G Z, et a1. Analysis of cascading failure in electric grid based on power flow entropy[J]. Physics Letters A, 2009, 373(34): 3032- 3040.
[19] 曹一家, 张宇栋, 林辉, 等. 基于同配性的电力系统自组织临界性识别[J]. 电力系统自动化设备, 2013, 33(7): 6-11, 18. Cao Yijia, Zhang Yudong, Lin Hui, et al. Power system self-organized criticality recognition based on assortativity[J]. Electric Power Automation Equipment, 2013, 33(7): 6-11, 18.
[20] 李蓉蓉, 张晔, 江全元. 复杂电力系统连锁故障的风险评估[J]. 电网技术, 2006, 30(10): 18-23. Li Rongrong, Zhang Ye, Jiang Quanyuan. Risk assess- ment for cascading failures of complex power system [J]. Power System Technology, 2006, 30(10): 18-23.