Based on the genuine four-particle entangled state (Yeo and Chua in Phys. Rev. Lett. 96:060502, 2006) and the ping-pong protocol (Boström and Felbinger in Phys. Rev. Lett. 89:187902, 2002), an efficient quantum dialogue scheme is proposed. Since some subtle methods are employed, the information leakage doesn't exist in this scheme. There are two security-check processes in this scheme, one is accomplished by using the four-particle entanglement pairs and the other by using the d particles.

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Aiming at the deficiencies of power sharing control performances when a traditional droop control is adopted for microgrid inverters, this paper proposes a microgrid inverter power sharing control strategy based on a virtual synchronous generator. This control method simulates the electromechanical transient characteristics of a synchronous generator in a power system by an ontology algorithm and the control laws of a synchronous generator by control over the speed governor and excitation regulator. As a result, that the microgrid system is able to effectively retain the stability of the voltage and frequency, and the power sharing precision of the microgrid inverter is improved. Based on an analysis of stability of a microgrid system controlled by a virtual synchronous generator, design thoughts are provided for further improvement of the power sharing precision of inverters. The simulation results shows that when the virtual synchronous generator based control strategy was adopted, the power sharing performances of microgrid inverters are improved more obviously than those using the droop control strategy.

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In distributed generation systems, islanding detection is a necessary function of grid-connected inverters. In view of the performance disadvantages of traditional passive and active islanding detection methods, this paper proposes a novel passive islanding detection method. The proposed method first extracts characteristic parameters from the inverter output voltage signal and inverter output current signal through lifting wavelet transform, and then conducts the pattern recognition of these extracted characteristic parameters via BP neural network, so as to judge if there is an islanding phenomenon. As verified by the simulation and experiment results, the islanding detection method proposed in this paper is effective, and is featured by high detection speed and small non-detection zone, without affecting electric energy quality; its detection performance has been remarkably improved in comparison with that of traditional islanding detection methods.