用于配电网多目标无功优化的改进粒子群优化算法

针对配电网多目标无功优化的应用需求以及优化算法存在的收敛性和多样性问题,基于Pareto熵的多目标粒子群优化算法,提出一种应用于多目标无功优化的改进粒子群优化算法。该算法在全局外部档案更新过程中引入冗余集策略,避免迭代过程中陷入局部最优解。将算法应用于配电网无功优化中时,采用离散变量取整方法,加快算法的收敛速度。建立网损、电压偏差及无功补偿装置投资最小的配电网多目标无功优化模型,并以IEEE 33节点配电网络为算例进行仿真,结果表明改进后的算法兼顾了优化的收敛性和多样性,能够在不同的优化要求下得到有效的无功优化方案。

Improved particle swarm optimization algorithm for multi-objective reactive power optimization of distribution network

Aiming at the application requirement of multi-objective reactive power optimization in distribution network and the convergence and diversity problems of optimization algorithms, an improved particle swarm optimization algorithm for multi-objective reactive power optimization is proposed based on the multi-objective particle swarm optimization algorithm with Pareto entropy, which introduces the redundancy set strategy in the process of global external file update to avoid falling into the local optimal solution in the iterative process. The discrete variable integral method is adopted to accelerate the convergence speed of the algorithm when it is applied to the reactive power optimization of distribution network. A multi-objective reactive power optimization model for distribution network with minimum network loss, voltage deviation and investment of reactive power compensation device is built, the IEEE 33-bus distribution network is taken as an example, and results show that the improved algorithm considers both the convergence and diversity of optimization, and can obtain effective reactive power optimization schemes under different optimization requirements.