Deng Zhixiang

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.

Capacity of a class of Dirty-Tape Gaussian orthogonal relay channel with state information at the source and relay

A Gaussian channel with additive interference that is causally known to the transmitter is called a Dirty-Tape Channel (DTC). In this paper, we consider a state-dependent dirty-tape Gaussian relay channel with orthogonal channels from the source to the relay and from the source and relay to the destination. The orthogonal channels are corrupted by two independent additive interferences causally known to both the source and relay. The lower and upper bounds of the channel capacity are established. The lower bound is obtained by employing superposition coding at the source, Partial Decode-and-Forward (PDF) relaying at the relay, and a strategy similar to that used by Shannon at the source and relay. The explicit capacity is characterised when the power of the relay is sufficiently large. Finally, several numerical examples are provided to illustrate the impact of additive interferences and the role of the relay in information transmission and in removing the interference.