摘要
针对广义负荷建模中逆变器容量限制带来模型泛化能力变差问题,对逆变器容量限制影响并网点广义负荷故障响应的机理进行了分析,提出将分段函数拟合的思想应用于广义负荷人工神经网络类模型的训练,即将各故障样本进行联合训练以同时学习其不同的分段特性。最后建模仿真结果表明所提方法能够同时较好地提高人工神经网络模型在广义负荷建模中的泛化能力和稳定性。
Aiming at the problem that the capacity limitation of inverter made the generalization ability of model worse in the generalized load modeling,the mechanism that how the inverter capacity limitation influenced the generalized load fault response of the grid-connected point was analyzed in this paper. The idea of fitting the piecewise function was proposed and applied to the training of the generalized load artificial neural network class model. Namely,fault samples were jointly trained at the same time to learn different segmentation characteristics. Finally,based on the modeling and simulation results,it is proved that the proposed method can improve the generalization ability and stability of the artificial neural network class model in generalized load modeling.
作者
郑秋宏
韩蓓
李国杰
Zheng Qiuhong;Han Bei;Li Guoji(Key Laboratory of Power Transmission and Power Conversion of Ministry of Education,Shanghai Jiao Tong University,Shanghai 200240,China)
出处
《电测与仪表》
北大核心
2020年第1期55-61,共7页
Electrical Measurement & Instrumentation
基金
国家电网公司总部科技项目(52094016000F)
关键词
总体测辨法
人工神经网络
逆变器容量限制
分段函数拟合
泛化能力
measurement-based modeling approach
artificial neural network
inverter capacity limitation
piecewise function fitting
generalization ability
