【作者】 蒋秀洁；

【导师】 熊信艮；

【作者基本信息】 华中科技大学 ， 电力系统及其自动化， 2004， 硕士

【摘要】 电力系统经济调度中一个重要的问题就是研究短期内发电资源经济调度问题,即机组优化组合问题。电力系统机组优化组合是电力系统优化运行理论中的重要内容之一,其优化目标是在保证电能质量和供电安全性,满足短期预测负荷、系统备用需求以及所有机组的物理和运行约束的前提下,确定调度周期内系统所有机组在各时刻的启停状态及发电功率,使系统的运行成本降至最低。它是一个非凸、不可微的非线性大系统混合整数规划问题,因此求解非常困难。但由于其所带来的显著经济效益,人们一直在积极研究,提出了各种方法来解决这个问题。本文首先概述了电力系统经济调度的特点、原理、国内外研究现状,描述了机组组合问题的数学模型,对现有的各种求解机组优化组合问题的方法进行了分析和综述,并比较了他们的优缺点。接着,探讨了一种新的群智能优化方法——粒子群优化(PSO)算法,详细介绍了PSO算法的基本原理及其各种改进技术。最后,在研究粒子群优化算法的基础上,针对电力系统机组优化组合的特点,对PSO算法做了相应的改进,提出了适合于求解电力系统机组组合优化问题的三种改进PSO算法:自适应PSO优化算法、改进的杂交PSO算法以及改进二进制PSO算法,并结合机组组合优化的数学模型,论述了改进PSO算法的理论基础,构造方法及实现过程。 粒子群优化(PSO)算法是一类随机全局优化技术,它通过粒子间的相互作用发现复杂搜索空间中的最优区域。PSO算法的优势在于操作简单,可调参数少易于实现而又功能强大。目前,PSO算法已成为国际演化计算界研究的热点。对粒子群优化算法的研究表明,它有着较高的搜索效率。将粒子群优化算法应用于解决电力系统机组优化组合问题,计算结果说明本算法是正确可行的,具有一定的适用价值。最后,针对PSO和GA单一优化算法的不足,结合两种算法的优点,形成了一种新的PSO组合算法(PSO-GA)。通过对新算法的测试,结果说明,组合PSO算法具有单一PSO算法和GA算法不可比拟的优势。根据故障定位的改进矩阵算法,分析了目前算法存在的问题,针对配电网末端故障及不同线路上的多重故障问题,提出了切实可行的判据,不仅能对配电网单一故障进行定位,而且能对配电网末端故障以及不同线路上的多重故障做出快速、准确的诊断。然后模拟计算了3电源配电网及单电源多出线的各种故障,结果表明了该判据的有效性。更多还原

【Abstract】 The unit commitment is one of the most important in the economy scheduling in power system.The unit commitment optimization is one of the fundamental issues in power system operation.It is used to determine when to start up and/or shut down units,how to dispatch the committed units to meet system-wide demand and reserve requirements during the scheduling horizon,and its optimal objective is to reduce the total operating costs.This dissertation focuses on this problem as well as resource scheduling with state constrains.The characteristic,principle,current status and development of the electric power system economic dispatch are generalized in this thesis.In the dissertation,a mixed-integer programming model for unit commitment is presented and some methods of solving this problem are summarized.Then,a new optimization technique originating from artificial life and evolutionary computation is researched,names particle swarm optimization (PSO) algorithm.The basic principle of PSO is introduced at length,and various improvements of PSO are also present.Finally,based of PSO research and the characteristic of unit commitment in power system,improvements of PSO are presented.They are adaptive partical swarm optimization(APSO) algorithm,improved hybrid partical swarm(HPSO) algorithm and improved binary partical swarm algorithm.Based on the mathematical model of the optimal unit commitment,the improvement of PSO principle,contriving means and realization process are presented.Particle swarm optimization is a stochastic global optimization technique.It finds optimal regions of complex search spaces through the interaction of individuals in a population of particles.Particle swarm optimization has become the hotspot of evolutionary computation because of its simple for implement,excellent performance and few parameters need to be tuned. Researching on PSO show that it has preferably searching ability.Applying those improvements PSO to the unit commitment optimization problem,the result of simulation is show that PSO is correct and useful,and the method proved to be practical.Finlly,because of the deficiency of single algorithm,a novel hybrid approach is proposed,namly PSO-GA based synthesizeing the merits in both PSO and GA.Simulated experiments for the optimization of nonlinear functions show that the PSO-GA algorithm is superior to the PSO and GA in both the speed of convergence and the ability of finding the global optimum.This paper analysis those known fault location arithmetic of discrimination.Based on <WP=5>current matrix arithmetic not locating fault section when faults occur at the ends of feeder branches and multiple faults in different feeder branches,a novel judgement based on this matrix arithmetic is presented and can solve these problem,which can not only locate single fault,but identified ends and multiple faults quickly and correctly.Simulating and analyzing various faults in the single source and multi source power distribution system,the result indictes its validity.更多还原