摘要
电力系统线路参数估计传统数值方法受到诸多限制。该文提出基于经验知识的参数估计目标函数修正项,导出状态估计与参数估计目标函数极值的关系,由此提出将目标函数从增广解空间垂直投影到参数空间的估计策略,并指出参数估计偏差的成因。针对投影的局部单峰特性,提出分段适应粒子群优化(staged particle swarm optimization,SPSO)算法。该算法根据群体适应度方差分阶段调整飞行参数,并在初始阶段增加新型免疫记忆算子。补充证明以估计值均值作无偏估计的可行性。仿真算例表明:应用该文修正项和SPSO方法,能快速准确的估计线路参数,并提高参数估计精度和降低对量测系统参数可估计性的要求,估计均值更接近参数真值。
Traditional numerical methods for line parameter estimation in power systems require too many ideal conditions. A knowledge-based correction term in the objective function is proposed. The relationship between state estimatin and parameter estimation is analyzed. A strategy of projecting the objective function from the augmented solution space to the parameter space is proposed and proved. Corresponding to the unimodality of the vertical projection, a staged particle swarm optimization（SPSO） is proposed. This algorithm can adjust flying parameters according to the fitness variance and absorb a novel immune memory operator during the first phase. The mean estimated values of the parameters are proposed as unbiased estimators. The examples indicate that the unknow parameters can be estimated faster and more accurately with the proposed objective function and SPSO, the solving procedure relies less on the augmented jacobian matrix, and the mean estimated values are close to the parameter truth values.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2008年第1期41-46,共6页
Proceedings of the CSEE
关键词
电力系统
状态估计
参数估计
粒子群优化
免疫记忆算子
power system
state estimation
parameter estimation
particle swarm optimisation
immune memory
