Deng Lihua

Characteristic model based micro-grid equivalent modeling

With the development of distributed and micro-grid technology, more and more micro-grid will connect into the power system. The dynamic simulation of distributed network should consider the affection of micro-grid. This paper proposes a micro-grid equivalent modeling method based on the characteristic model. The characteristic model can simplify the detailed model of micro-grid without missing the characteristic information under connected mode. Characteristic model is presented by a low-order time-varying difference equation and the difference equations are equal to the micro-grid dynamic characteristic. Voltage and power of the Point of Common Couple(PCC) are collected for the input and output of the characteristic model respectively. Recursive damped least square algorithm is used for the parameter estimation and the measured vector is normalized for improving the convergence of the algorithm. An micro-grid system is built for the simulation of micro-grid in DIgSILENT, and simulation results show that the dynamic equivalent based on characteristic model can well describe the dynamic characteristic of the detailed model of micro-grid, and the modeling method is validity.

Micro-grid dynamic modeling based on RBF Artificial Neural Network

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.

DC microgrid dynamic equivalent modeling based on the measured data

Microgrid is a good platform for the application of distributed generation system in power system. Based on the main voltage, there are two types of microgrid: AC microgrid and DC microgrid. Micro-grid has two operation modes: grid-connected and isolated operation. It is expected that more and more DC microgrids will be connected into the distributed grid in the near future. Thus, an equivalent model of DC microgrid is necessary for the digital simulation for distributed grid. In this paper, an equivalent modeling method based on Radial basis function(RBF) artificial neural network(ANN) and bacterial foraging algorithm is proposed, which can descript the dynamic characteristic of micro-grid with a reduced order function. In order to improve the feasibility and reliability of the equivalent model, the voltage, current and power data of the PCC are collected and used as the training data for the RBF artificial neural network. The bacterial foraging algorithm is used for the parameter optimization of BRF-ANN. A DC microgrid contains different distribution generations is used to verify the proposed method. The results of the testes show that the proposed method is suitable and reliable for the DC microgrid, and the comparisons between the detailed model and equivalent model shows the accuracy of the equivalent model.