改进差分进化算法在电力系统无功优化中的应用

Application of Improved Differential Evolution Algorithm in Reactive Power Optimization

针对电力系统无功优化具有非线性、多控制变量、多约束条件、连续变量和离散变量混杂的特点,提出了一种改进的差分进化算法。该算法根据进化学习过程中积累的经验,利用优良群体引导变异的方向,同时提取优良群体各维元素的信息,以优良群体信息指导个体每一维变量的交叉操作。IEEE 30节点系统算例表明,所提算法较基本差分进化算法和粒子群算法,收敛速度快、计算精度高、稳定性好、能有效地求解电力系统无功优化问题。

In allusion to such features as nonlinearity, multi control variables, multi constraints and coexistence of continuous variables and discrete variables in power system reactive power optimization, an improved differential evolution algorithm is proposed. According to the accumulated experiences in evolutionary learning process and utilizing excellent group to lead the direction of mutation, the proposed algorithm simultaneously extracts information of each dimensional element of excellent group and guides crossover operation of each dimensional variable of individual by excellent group information. Calculation results of IEEE 30-bus system show that the proposed algorithm can effectively solve power system reactive power optimization problem and in aspects of convergence speed, calculation accuracy and stability the proposed algorithm is better than particle swarm optimization algorithm and the standard differential evolution.