模糊熵权法和CCPSO算法的含风电场电力系统多目标无功优化

Multi-objective Reactive Power Optimization in Electric Power System with Wind Farm Based on Fuzzy Entropy Weight Method and CCPSO Algorithm

研究了含双馈风电机组的电力系统多目标无功优化问题,分析了双馈风电机组的无功特性,将风电场作为连续的无功调节手段参与电力系统无功优化.提出一种基于模糊商权法和组合混沌序列动态粒子群算法(CCPSO)的多目标无功优化方法.利用模糊熵权法解决主观权值和客观权值的缺陷,建立以静态电压稳定浴度、节点电压和有功网损为目标的含风电场多目标无功优化决策模型.针对利用传统粒子群算法进行求解时,易出现局部收敛等问题,结合Chebyshev映射和Logistic映射,在粒子初始化过程中运用一种组合混沌映射,增强初始粒子的均匀性,同时将Logistic混沌优化引入到算法寻优过程中,使算法全局寻优能力得到加强.以IEEE14节点系统为例进行仿真实验,验证所提方法的有效性和实用性.

A study is conducted on the reactive power optimization in distribution network of the wind farm. The reactive power characteristics of the double-fed wind turbine are analyzed, and the wind farm is used as a continuous reactive power adjustment means to participate in the reactive power optimization of the power system.A multi-objective reactive power optimization method is proposed based on fuzzy quotient and chaotic sequence dynamic particle swarm optimization algorithm. The fuzzy entropy weight method is used to solve the defects of subjective weight and objective weight, and a multi-objective reactive power optimization decision model with wind power is proposed based on static voltage stability margin, voltage deviation and active power loss. In order to solve the local convergence with traditional particle swarm algorithm, combined with Chebyshev mapping and Logistic mapping, a combinatorial chaotic mapping is used to enhance the homogeneity of the initial particles in the particle initialization, and Logistic chaos optimization is introduced to the process of algorithm optimization,strengthening the global optimization ability of the algorithm. The IEEE14-node system is used as a test case, and the result proves the feasibility and validity of the proposed method.