基于改进量子粒子群算法的风电场并网电力系统的无功优化

发布时间：2018-03-02 17:26

本文选题：无功优化　切入点：有功损耗　出处：《南京邮电大学》2017年硕士论文　论文类型：学位论文

【摘要】：当前经济发展迅猛,因对电力的需求量逐渐增加,而使电网系统的安全方面的问题也日益凸显。而电能质量的优劣是确保整个电力系统能否安全平稳运行的关键,电压质量又作为衡量电能质量的一个重要参考项,因此该指标逐渐成为电力工作人员关注的重点。同时影响电压质量的要素包括无功优化,因而对电力系统采取合理的无功规划尤为关键。合理的无功优化,在有效的改善整个电网的无功分布的同时,还能在一定程度上降低系统的有功损耗。电力系统无功优化是一繁杂的非线性规划问题,涉及到多个变量和多条约束条件,同时在变量中离散和连续变量均有。对于在约束条件中存在的等式约束,它作为一个高阶的非凸方程组,同样包含了多个变量和约束条件。若采用一般的数学方法进行求解,则整个计算过程复杂且低效,因此需要选择合理的方法进行计算求解。对于在无功优化过程中,可以采用调整发电机的端电压值、可投电容的无功补偿以及可调变压器变比值的措施,实现减少电力系统有功损耗的优化目标,使电网安全平稳工作。本文通过对粒子群算法和量子粒子群算法的研究,对于两种算法在迭代寻优后期,均有易陷入局部最优、后期收敛速度减缓等缺陷,提出一种改进的量子粒子群算法。该改进算法是基于交叉因子的双向寻优策略,将粒子种群全局最差点的反向点引入量子粒子群算法中粒子的位置更新公式中,从而帮助粒子在后期迭代过程中能够跳出局部最优。接着本文选择将有功网损纳入建模核心,目标函数定为使电网有功网损尽量降低,再结合罚函数建立数学模型,用来处理目标函数中出现的电压越界和发电机无功越界的问题,再进一步应用到风电场中,对其进行无功优化。最后列出将改进量子粒子群算法应用于含风电场并网的电力系统无功优化的算法步骤。选取以IEEE57节点系统作为业务应用场景,将改进量子粒子群算法应用于此场景中,并和PSO算法和QPSO算法比较,由仿真结果表明该改进算法有效的减少了电力系统的有功网损。
[Abstract]:With the rapid economic development, the demand for power is increasing gradually, and the security problems of the power system are becoming increasingly prominent. The quality of power is the key to ensure the safe and stable operation of the whole power system. Voltage quality is regarded as an important reference item to measure power quality, so this index has gradually become the focus of attention of power workers. At the same time, the factors that affect voltage quality include reactive power optimization. Therefore, it is very important to take reasonable reactive power planning for power system. Reasonable reactive power optimization can effectively improve the reactive power distribution of the whole power system. Reactive power optimization is a complicated nonlinear programming problem, which involves many variables and constraints. There are both discrete and continuous variables in the variables. As a higher order nonconvex system of equations, it also contains many variables and constraints for the equality constraints that exist in the constraint conditions. If the general mathematical method is used to solve the problem, The whole calculation process is complex and inefficient, so it is necessary to select a reasonable method to solve the problem. In the process of reactive power optimization, the terminal voltage of the generator can be adjusted. The reactive power compensation of capacitors and the variable ratio of transformers realize the optimization goal of reducing the active power loss in power system and make the power grid work safely and stably. In this paper, the particle swarm optimization algorithm and quantum particle swarm optimization algorithm are studied. In this paper, an improved Quantum Particle Swarm Optimization (QPSO) algorithm is proposed, which is based on the crossover factor. The reverse point of global worst point of particle population is introduced into the updating formula of particle position in quantum particle swarm optimization algorithm, so that particle can jump out of the local optimum in the late iteration process. Then, the active power network loss is selected to be included in the modeling core in this paper. The objective function is to minimize the loss of the active power network, and a mathematical model is established by combining the penalty function, which is used to deal with the problem of voltage and generator reactive power crossing in the objective function, and further applied to wind farm. Finally, the steps of applying the improved quantum particle swarm optimization algorithm to the reactive power optimization of the power system with wind farm connected to the grid are listed. The IEEE57 node system is selected as the service application scenario. The improved Quantum Particle Swarm Optimization (QPSO) algorithm is applied to this scenario and compared with the PSO algorithm and the QPSO algorithm. The simulation results show that the improved QPSO algorithm can effectively reduce the active power loss of the power system.
【学位授予单位】：南京邮电大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18;TM614;TM714.3

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