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1、华中科技大学博士学位论文基于复杂网络理论的电力系统网络模型及网络性能分析的研究姓名：史进申请学位级别：博士专业：电力系统及其自动化指导教师：涂光瑜20080606I摘 要 电力系统网络是一个有着大量节点、节点之间有着复杂连接关系的网络，它具有复杂网络的一般特征。正是由于电力系统具有复杂性的特征，随着我国电网的地区网络互联的展开，电网变得越来越复杂，大电网的复杂性和大电网运行的安全受到了越来越多的关注。 在其他领域已有很多根据各自特点建立的复杂网络静态模型，因此，结合电力系统自身特点进行网络简化，引入具有电力系统特征的物理参数来建立电力系统复杂网络模型是十分必要的。本文在对电力系统的自有特征研究
2、的基础上，进行电力系统复杂网络模型的改进，提出具有电力系统自有特征的电力系统复杂网络建模方法，建立了电力系统复杂网络加权模型，同时也提出相应的加权复杂网络统计特征指标，为研究电力系统复杂网络的特性建立了研究基础。 本文首先介绍复杂网络的基本原理，给出复杂网络的基本模型与一些常用的拓扑构造模型。在对电力系统复杂网络的研究现状进行综述的基础上，指出电力系统复杂网络模型研究现阶段关注的问题。 本文接下来从两个方面改进了电力系统复杂网络的基本模型，以期更多地描述电力系统有别于其他复杂网络的特性。在拓扑建模时改进复杂网络距离的定义，改进了相应的统计特征指标得到电力系统复杂网络非加权网络模型；然后在非加权
3、网络模型的基础上，提出与复杂网络基本加权方法不同的电力系统复杂网络的加权方法，得到电力系统复杂网络加权模型， 同时也提出了相应的加权复杂网络统计特征指标。 随后，将大量 IEEE 模型用于电力系统复杂网络特性仿真研究，通过对计算得到的几种模型下电力系统复杂网络统计特征指标的内在联系进行了详细的分析，同时针对复杂网络的小世界特性和无标度特性展开研究。 本文在提出网络连通性能这一电力系统复杂网络的特殊统计指标之后，将之应用于关键节点和关键线路的辨识。应用 IEEE57 节点算例来进行仿真计算，将不同辨识方法得到的结果进行对比分析，进行辨识方法有效性的验证。 II随后，本文介绍现有的电力系统复杂网络
4、动态演化模型，然后采用概率模型的建立方法，以电力系统复杂网络加权模型为基础，提出了电力系统消去动态演化模型，详细描述演化中可能存在的过程。在随后的工作中，引入了风险理论，将其与电力系统复杂网络的动态演化模型相结合，用于电力系统安全综合评估，提出了风险指标，并采用 IEEE57 节点模型作为仿真算例。 最后，本文对所作的工作进行了总结，并对电力系统复杂网络的研究发展作出了展望。 关键词： 电力系统复杂网络，加权网络，统计特征指标，小世界，无标度， 动态演化模型，风险指标 IIIAbstract The power system network consists of a mass of node
5、s and the connections between nodes are seriously complex, so it has the common characteristic of complex network. The network connections of local power systems increase the complexity of the power network. Due to the complex characteristic of the power system, more and more attention is paid to th
6、e complexity investigation of power system. In other fields, there are some static models in terms of respective filed characteristic. As a result, it is significantly important to simplify the power system network according to the power system characteristic and set up an available power system com
7、plex network model by introducing some physical parameters with power system characteristic. In particular, this paper investigates the inherent characteristic of power system and improves the complex network model of power system. Furthermore, the modeling method with power system inherent characte
8、ristic is proposed and the weighted model to complex network of power system is presented. At the same time, the relevant statistical characteristic indices are addressed. The research on the characteristic of power system complex network is based on the above model. Firstly, the paper introduces th
9、e fundamental principle of complex network. Some fundamental complex network models and topologies are described. And then, the research background on power system complex network is presented and state of art in the research on complex network model is indicated. Secondly, the fundamental model of
10、power system complex network is modified from two parts and the special characteristic different with other complex network is described in detail. The modified model is achieved by the following method. The distance definition of complex network is modified when the corresponding model is construct
11、ed. The non-weighted network model of power system complex network is established by modifying the corresponding statistical characteristic index. And then, based on the non-weighted network model, a new weighted method of power system complex network different with fundamental weighted method is pr
12、oposed and the weighted model of power system complex network is provided, as well as the corresponding statistical characteristic index of the weighted network can be obtained. Furthermore, many IEEE models for power system are applied for the research on power system complex network characteristic
13、. The IVrelationship between statistical characteristic indices of power system complex network on several models achieved by detailed calculation is analyzed. The small world model and scale-free characteristic of complex network are also concerned at the same time. Thirdly, the paper proposes a ne
14、twork connection performance index which is a special index for power system complex network. And then, the special index is applied to identify the key node and line in power system in this paper. In addition, the IEEE 57 nodes example is used to carry out the simulation to verify the viability and
15、 efficacy of the new identification method by comparing with the results from different identification methods Fourthly, the current dynamics evolutionary models for power system are introduced. In terms of the weighted model of power system complex network, the elimination dynamics evolutionary mod
16、el for power system is addressed by applying the method of establishing model. Accordingly, the potential processes on the evolutionary are described in detail. In the following research, the risk theory is introduced and combined with the elimination dynamics evolutionary model for power system to use for the comprehensive assessment of power system safety. As a result, the risk index is advanced. In the same way, the IEEE 57 nodes example is used to achieve the simulation to verify the effe
