【作者】 杨琳；

【导师】 李娟；

【作者基本信息】 东北电力大学 ， 电力系统及其自动化， 2011， 硕士

【摘要】 电力系统的无功优化控制,不仅能有效地降低系统的有功功率损耗,而且还可以改善电网的电压质量,对系统的安全稳定、经济运行具有非常重要意义。根据系统运行的要求,无功优化问题可以分为单目标无功优化和多目标无功优化。多目标无功优化顺应电力系统运行的经济性和安全性的要求。本文将有功功率损耗最小、节点电压偏移量最小和静态电压稳定裕度最大整合为无功优化的目标函数。分析粒子群算法的构成及寻优原理,指出基本粒子群优化算法由于随机生成代表控制变量值的粒子,使得在优化迭代过程中易陷入局部最优解,而且后期收敛速度慢等问题,将混沌优化算法融合到粒子群算法中,提出了自适应混沌粒子群算法求解多目标无功优化问题。该算法在初始化粒子即无功优化控制变量值时,采用混沌思想,增加控制变量取值的多样性;通过粒子群无功优化算法计算各个粒子对应的适应值即无功优化目标函数值,并按照其大小择优选取控制变量值进行混沌优化以帮助无功优化控制变量跳出局部极值区域;并根据无功优化目标函数值自适应地调整其惯性权重系数以提高全局与局部搜索能力。将自适应混沌粒子群算法应用到多目标无功优化中,通过MATLAB编制程序对IEEE14和IEEE30节点系统进行无功优化计算,并与基本粒子群算法和遗传算法比较,结果表明本文提出的算法具有很好的全局寻优能力和较快的收敛速度,能够有效地进行无功优化。按优化的结果进行的系统运行控制,能达到降低网损,提高电压质量水平和静态电压稳定性的目的。更多还原

【Abstract】 The control of reactive power optimization in electric power system can not only reduce power loss,but also improve voltage quality. Consequently it is of great importance to security and economic operation of power system.According to the requirements of power system operation, reactive power optimization problem can be divided into single-objective reactive power optimization and multi-objective one. To meet the needs of economic and safe operation of power system, this paper establishes the minimum set of system power loss, the minimum node voltage offset and the largest static voltage stability margin in the multi-objective reactive power optimization model.The Particle Swarm Optimization Algorithm is mainly studied in the paper. And the composition of particle swarm optimization algorithm and the optimization principle were analyzed deeply. In particle swarm reactive power optimization it is easily falls into the local optimal solution in the iterative process of optimization due to the particles randomly generated on behalf of variable values and the slow convergence problem finally. Consequently, in this paper the chaotic particle swarm algorithm produced by the chaotic optimization algorithm integrated into the particle swarm algorithm is proposed to solve the problem of multi-objective reactive power optimization. The chaotic method is adopted to increase the diversity of control variable value in the initialization of the algorithm, that the value of reactive power optimization of control variables. The particle swarm algorithm is used to calculate the fitness of each particle that corresponds to reactive power optimization of the objective function value and merit-based selection in accordance with its size control variable values of chaos optimization to help optimize the reactive power control variables out of local optimum area. In accordance with the objective function value of reactive power optimization, it is adaptively adjusted its coefficient of inertia weight to enhance the global and local search capabilities.The chaotic particle swarm algorithm is applied to reactive power optimization,through MATLAB programming right IEEE14-bus and IEEE30-bus system reactive power optimization calculation,and with elementary particle swarm optimization and genetic algorithm comparison, the results show that the algorithm proposed in this paper effectively reduces the system power loss and improves system voltage quality level,and has a better ability of global optimization and faster convergence speed.更多还原