基于改进粒子群算法的电池储能系统多控制器参数优化

Improved PSO-based Parameters Optimization of ESS’s Multi-Controllers

针对电池储能系统的有功和无功多控制器参数同时优化易互相影响的问题,文章提出一种改进的粒子群算法(Improved Particle Swarm Optimization,IPSO)进行有功、无功多控制器的比例积分(Proportional Integral,PI)环节参数优化整定。IPSO算法通过采取全局领域搜索的速度更新方式增加搜索范围;在粒子群初始化加入混沌思想与优化过程加入混沌扰动以提升算法空间内搜索的遍历性;对全局最优粒子增加随机学习的学习机制加强寻找全局最优值的能力。基于IPSO算法,以多控制器的综合时间与绝对误差(Integrated Time and Absolute Error,ITAE)指标最小为目标函数对P-Q控制的PI控制器参数进行优化,并以储能系统接入IEEE-14节点系统为例进行仿真。仿真结果表明,IPSO算法相较其他PSO算法具有更好的优化效果,且寻优能力更强。

Iming at the problem that the parameters of active power and reactive(PQ)power multicontroller in energy storage system are easy to be influenced by each other,an improved particle swarm optimization(IPSO)algorithm is proposed to optimize the PI parameters of active power and reactive power multi-controller.The IPSO algorithm increases the search range by adopting the speed update method of global domain search,and adds chaos thought to particle swarm initialization and chaos disturbance to improve the ergodicity of search in the algorithm space.For global optimal particles,the learning mechanism of random learning is added to enhance the ability to find global optimal values.Based on the IPSO algorithm,the PI controller parameters of the PQ control are optimized with the minimum integrated time and absolute error(ITAE)index of the multi-controller as the objective function,and the energy storage system is connected to the IEEE-14 system as an example for simulation.The simulation results show that the IPSO algorithm has a better optimization ability and is able to find better results.