摘要
采用具有全局寻优能力的微粒群优化(PSO)算法辨识负荷模型的参数;同时考虑负荷电压的变化,用动态修正法实时修正负荷模型的参数,建模仿真分析结果验证了PSO-动态修正算法的有效性和准确性.相对于线性回归分析的动态修正法,该算法能够提高负荷模型的辨识精度,所建模型更适合描述全电压范围下负荷的静态特性.
The Particle Swarm Optimization （PSO） algorithm with global optimization ability is applied for load model parameters identification in this paper. The parameters are real-time corrected by the dynamic modification method with the load voltage change considering. Simulation analysis results verify the validity and feasibility of PSO-dynamic algorithm. Comparing with the linear regression analysis based dynamic modification method, the algorithm can enhance the identification accuracy of load model, and the established model is suitable for the description of load static characteristics in large range voltage.
出处
《电力科学与技术学报》
CAS
2009年第3期54-57,共4页
Journal of Electric Power Science And Technology
基金
国家自然科学基金(70601003)
