基于改进粒子群算法的电力系统无功优化研究

发布时间：2018-05-29 19:04

本文选题：无功优化 + 粒子群算法　；参考：《大连海事大学》2014年硕士论文

【摘要】：电力系统无功功率的合理分布是保证电压质量和降低网损的前提,对整个电力系统的安全稳定和经济运行有着重要的意义。无功优化是一个含有连续变量和离散变量的混合优化问题,具有多约束,非线性,高维度的特点。传统的优化算法将模型做了近似处理,离散变量连续化,导致有一定的误差,且求解复杂,计算效率降低。近年来,智能算法凭借其简单易行,鲁棒性强等特点得到了广泛的关注和研究,在电力系统无功优化方面也得到了应用。粒子群算法(PSO)是众多智能算法中性能较为优越的一种,具有收敛速度快,优化结果精度高和容易实现等优势。但基本粒子群算法在处理无功优化这类多维多峰的实际问题时,易陷入早熟,收敛速度和精度往往无法保证,可靠性不能保障。在此背景下,本论文针对无功优化问题的特点,以有功网损最小作为目标函数,在现有的粒子群算法基础上加以改进：首先,以线性递减权重系数的粒子群算法为基础,提出自适应权重系数的混沌粒子群算法(SAW-CPSO)。将线性递减惯性权重改为自适应惯性权重,引入混沌机制,用立方混沌对种群进行初始化,增加收敛速度和种群多样性,有效避免早熟。然后,对现有粒子群算法的拓扑结构进行了介绍和分析,在环形(Ring)拓扑的基础上,提出了一种新的具有动态拓扑结构的粒子群算法(D-PSO)。该方法在初始阶段采用聚类方法将粒子群分簇,加快收敛速度；在迭代过程中,根据各粒子簇的收敛情况引入邻居动态调整机制；在迭代后期,形成一个环形(Ring)拓扑,以提高算法全局搜索能力和收敛精度。最后,在Matlab软件平台下,分别将所提出的两种算法在无功优化问题上进行了应用,采用IEEE14, IEEE30节点的算例,并与其他粒子群算法进行了比较。优化结果证明所提出的方法在处理电力系统无功优化问题时具有可行性及有效性。
[Abstract]:The rational distribution of reactive power in power system is the premise to guarantee voltage quality and reduce network loss, which is of great significance to the safety, stability and economic operation of the whole power system. Reactive power optimization is a hybrid optimization problem with continuous and discrete variables, which has the characteristics of multi-constraint, nonlinear and high-dimensional. The traditional optimization algorithm approximates the model, and the discrete variables are continuous, which leads to some errors, complex solution and low computational efficiency. In recent years, due to its simplicity and robustness, intelligent algorithm has been widely studied and applied in reactive power optimization of power system. Particle Swarm Optimization (PSO) is one of the most intelligent algorithms, which has the advantages of fast convergence, high precision and easy implementation. But the basic particle swarm optimization algorithm is easy to fall into premature convergence speed and precision and reliability can not be guaranteed when dealing with the practical problems of multi-dimensional and multi-peak reactive power optimization. Under this background, aiming at the characteristics of reactive power optimization problem, this paper takes the minimum active power loss as the objective function and improves on the existing particle swarm optimization algorithm. Firstly, it is based on the particle swarm optimization algorithm with linear decreasing weight coefficient. An adaptive weight coefficient chaotic particle swarm optimization algorithm (SAW-CPSO) is proposed. The linear decreasing inertia weight is changed into adaptive inertia weight and chaotic mechanism is introduced to initialize the population with cubic chaos to increase the convergence speed and diversity of the population and to avoid precocity effectively. Then, the topological structure of the existing particle swarm optimization algorithm is introduced and analyzed. Based on the ring topology, a new particle swarm optimization algorithm with dynamic topology is proposed. In the initial stage, the clustering method is used to cluster the particle swarm to accelerate the convergence rate; in the iterative process, the neighbor dynamic adjustment mechanism is introduced according to the convergence of each cluster; in the later stage of the iteration, a ring topology is formed. In order to improve the global search ability and convergence accuracy of the algorithm. Finally, the two algorithms are applied to the reactive power optimization problem on the Matlab software platform. The examples of IEEE 14 and IEEE30 nodes are used and compared with other particle swarm optimization algorithms. The optimization results show that the proposed method is feasible and effective in dealing with reactive power optimization problems in power systems.
【学位授予单位】：大连海事大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM714.3

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