基于自学习迁移粒子群算法及高斯罚函数的无功优化方法

发布时间：2018-04-26 01:06

本文选题：云模型 + 迁移操作　；参考：《电网技术》2014年12期

【摘要】：针对粒子群算法在求解无功优化问题时存在早熟收敛,易陷于局部最优的现象,提出了自学习迁移粒子群算法(self-learning migration particle swarm optimization,SLMPSO)。该算法在采用混沌序列对粒子群进行初始化操作,基于云模型理论的X-条件云发生器对粒子的惯性权重进行自适应调整的基础上,引入一种迁移操作,以引导全局最优粒子的飞行方向,解决粒子群后期朝单一进化方向进化的问题,有效地增强了算法的全局寻优能力。针对电力系统无功优化中的离散变量归整问题,首先将离散变量完全化为连续变量进行迭代求解,在寻求至全局最优解后引入高斯罚函数对离散变量进行归整操作。以网损和电压偏离最小为目标,对IEEE标准30节点算例进行仿真计算,验证了所提算法的有效性和可行性。
[Abstract]:Aiming at the phenomenon that particle swarm optimization (PSO) has premature convergence and is prone to be trapped in local optimum in solving reactive power optimization problem, a self-learning migration particle swarm optimization (SLMPSOO) algorithm is proposed in this paper. This algorithm introduces a migration operation on the basis of initializing particle swarm by chaotic sequence and adjusting the inertia weight of particle by X- conditional cloud generator based on cloud model theory. In order to guide the flight direction of the global optimal particle and solve the problem that the particle swarm evolves towards a single evolutionary direction in the later stage, the global optimization ability of the algorithm is effectively enhanced. In order to solve the problem of discrete variables in reactive power optimization, the discrete variables are completely transformed into continuous variables to be solved iteratively, and Gao Si penalty function is introduced to correct the discrete variables after seeking the global optimal solution. Aiming at minimum network loss and voltage deviation, the IEEE standard 30-node example is simulated to verify the effectiveness and feasibility of the proposed algorithm.
【作者单位】：武汉大学电气工程学院;贵州电力试验研究院;
【基金】：国家科技支撑计划(2013BAA02B02)~~
【分类号】：TM714.3

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