【作者】 程涛；

【导师】 黄彦全；

【作者基本信息】 西南交通大学 ， 电力系统及其自动化， 2008， 硕士

【摘要】 随着电力工业的迅速发展,电力系统的结构和运行方式日趋复杂。因此,现代化的调度系统要求能迅速、准确而全面地掌握电力系统的实际运行状态,预测和分析系统的运行趋势,对运行中发生的各种问题提出对策。而保证电力系统实时数据的质量是进一步提高在线应用水平的关键。同步相量测量技术是现代电力工业最重要的技术之一。卫星技术、计算机和通信技术的发展使相角测量有了一定的基础,尤其自1993年全球定位系统GPS全面民用化以来,基于GPS的同步相量测量单元(PMU)实现了电力系统的功角和母线电压相角直接测量。由于PMU量测具有精度高,全网严格同步,更新周期短等优点,所以同步相角测量技术在电力系统动态监视、安全稳定分析、控制和状态估计方面发挥着越来越重要的作用。电力系统的状态估计是能量管理系统和在线决策系统的重要组成部分,EMS的其它高级应用程序均依赖于状态估计的结果。但是在实际应用中,由于电力系统地域辽阔,如果要在所有的变电所安装相角测量单元,将会大大增加系统的一次性投资。因此,从经济性的角度出发,应该采用分期投资的方式,即分阶段在变电站安装相角测量装置。这就应该确定在哪些地方安装相角测量装置,即安装地点和安装数量的优化问题。组合优化的方法有很多,但是传统的搜索优化方法工作量大,不能保证寻到的解是最优解或全局最优解。遗传算法(Genetic Algorithm,简称GA)是以自然选择和遗传理论为基础,将生物进化过程中适者生存规则与群体内部染色体的随机信息交换机制相结合的高效全局寻优算法,GA摈弃了传统的搜索方式,模拟自然界生物进化过程,采用人工进化方式对目标空间进行随机优化搜索。是一种具有广泛适应性的搜索优化方法。本文在传统状态估计的基础上,建立了引入PMU相角测量数据以后的混合状态估计模型。基于此模型,定量地分析了PMU的放置位置和配置数量对电压相角状态估计精度的影响以及量测噪声的大小对配置PMU会有什么样的影响。进而提出了采用遗传算法,以提高状态估计的精度为目标,优化电力系统相角测量装置的安装地点及数量,使得系统在相角测量装置安转点数最少的情况下,状态估计精度最高。并采用IEEE14节点网路对算法的可行性进行了验证。更多还原

【Abstract】 Along with the rapid development of power industry, the structure and operating mode are growing complexity. So modern scheduling system require rapid, accurate and comprehensive grasp of the operating state of the power system, forcasting and trend analysis of the operation system, running on the various issues put forward countermeasures. Guarantee the quality of the real-time datebase is to enhance the level of critical on line appplicatons.The synchronous phasor measurement technology is one of the most important technologies in modern electric power industry. Satellite, computer and communication technology’s development, lays a foundation for the development of phasor measurement. Especially, since the 1993, the global position system (GPS) becomes popular. Because of its high accuracy, high reliability and high accuracy on timing, the technoloty of synchronous phasor measurement plays a major role in dynamic monitoring, security stability analysis and control and state estimation (SE) in power system. SE is the signify partition of energy management system (EMS). The other high lever application software of EMS are influenced by the performance of the SE.However, in priactical application, the power system is vast in area, if we have all substation installation of PMU, the system will greatly increase the one-time investment. Therefore, from the economic point of view, should adopt a phased investment approach, that is phase angle measuring device should be installed in a phased manner. This stage should be used in determining what areas should be installed phase angle measureing device, that is a optimization problem of the number and sits of installation.Combinatorial optimization has many ways, but the traditional search optimization method are heavy workload, can not gurantee that the optinal solution is the best or global solution. Genetic algorithm is the theory of natural selection and genetic basis of the process of the biological evolution, it is an efficient global optimization algorithm which combinate the survival of fittest rules of and the internal gropus of random chromosome information exchange mechanism. GA abandon the traditional the search, simulated the process of evolution in nature, using artificial evolution methods on the target space stochastic optimization search. Genetic algorithm is a broad search optimization method.On the basis of the traditional state estimation, this paper established a fixed state estimation model. Based on the model, the relation between the number and sits of PMU and error of the state estimation can be annlyzed quantitatively. Then, based in the research, Genetic algorithm is proposed to opitimize the phase angle measurement unit placement in order to improve the accuracy of state estimate and minimize the amount of phase angle measurement. The feasibility of the method was verified by using IEEE14 node networks.更多还原