摘要
提出了一种用于电力系统负荷建模和参数辨识的遗传进化算法，该方法与传统的最小二乘法相比具有全局搜索优化特点，适用于非线性、不连续或微分不连续的各种负荷模型。该方法已成功用于工业负荷实测数据辨识及动态和静态负荷建模。在静态负荷建模上，辨识结果略优于传统的最小二乘法，且通用性更好，只需做极小的修改就可以用于各种形式的静态负荷模型。在动态负荷建模上算法不仅给出了更优秀的结果，而且表现出很好的稳健性。结果表明此方法在负荷建模中的优势。
This paper proposes an evolutionary programming (EP) algorithm applying to load modeling and parameter identification. Compared with conventional methods, EP algorithm is a search method that can be used for nonlinear and discontinuous problems. Cases of both static and dynamic load data from field experiments are studied. For static load modeling the identification result of EP method is better than that of the least square method. It can be used for different kinds of static load models with minor modification; which shows its universality. The exact and robust results show the advantage of this method in dynamic load modeling and parameter identification.
出处
《清华大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
1999年第3期37-40,共4页
Journal of Tsinghua University(Science and Technology)
基金
国家攀登计划
