Title :
Micro-grid dynamic modeling based on RBF Artificial Neural Network
Author :
Cai Changchun ; Wu Min ; Deng Lihua ; Deng Zhixiang ; Zhang Jianyong
Author_Institution :
Hohai Univ., Changzhou, China
Abstract :
A simplified equivalent model of microgrid, based on the RBF Artificial Neural Network, is present in this paper. The proposed model is suitable for the dynamic studies of microgrids. Nonlinear mapping of RBF neural network describes the dynamic characteristics of the Point of Common Couple(PCC) of micro-grid under the connected mode. The development model is evaluated using the voltage, current and power of the PCC as the input and output of the RBF neural network in the train process. The PSO algorithm is used for the parameter optimization of RBF and improved the generalization of the dynamic model. The simulation results show the proposed modeling method in this paper is suitable and effective, and the RBF neural network based dynamic model can describe the dynamic characteristics of micro-grid accurately.
Keywords :
distributed power generation; particle swarm optimisation; power engineering computing; radial basis function networks; PCC; PSO algorithm; RBF artificial neural network; development model; microgrid dynamic modeling; nonlinear mapping; parameter optimization; particle swarm optimization algorithm; point of common couple; simplified equivalent model; train process; Analytical models; Distributed power generation; Heuristic algorithms; Load modeling; Neural networks; Optimization; Power system dynamics; Equivalent Modeling; Micro-grid; Particle swarm optimization algorithm; RBF neural network;
Conference_Titel :
Power System Technology (POWERCON), 2014 International Conference on
Conference_Location :
Chengdu
DOI :
10.1109/POWERCON.2014.6993926
