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Communication data in the communication network of the islanded micro‐grid can be disturbed in many ways, such as data attack or packet loss. To improve the control effect on the voltages and frequencies obtained by droop control of distributed energy resources (DERs) facing communication data disturbance (CDD), a response hierarchical control strategy is proposed in this study. Based on the concept of the cyber physical system, the control structure is divided into two layers: the cyber layer and physical layer. In the cyber layer, firstly, the effect of the CDD on the micro‐grid system is analysed and then, an event‐triggered data compensation method combining the back‐propagation neural network and extreme learning machine is proposed in this layer to solve the problem of the CDD. In the physical layer, firstly, the droop control is used as the primary control to control the voltages and frequencies of DERs and then, a novel virtual leader‐following consensus control method considering time‐delay is proposed in this layer. Also, it is used to complete the secondary control of the voltage and frequency obtained by primary control. In the end, the simulation results confirm the effectiveness of the proposed hierarchical control strategy under CDD. [ABSTRACT FROM AUTHOR]

Accurate renewable resource and load forecasting plays a key role in the progress of power grid planning schemes. In this study, a hybrid short‐term forecasting method based K ‐means clustering and variational mode decomposition (VMD) technique is proposed to deal with the problem of forecasting accuracy. K ‐means clustering is a means of data mining approach and used for classifying data into several clusters. A cluster selection method is adopted to extract similar features from historical days. To better analyse the time series of historical data, VMD decomposes time series data into an ensemble of components with different frequencies. Self‐adaptive evolutionary extreme learning machine as a novel and fast regression tool is trained and used for predicting each component. Eventually, the forecasting result generated by reconstructing all the predicted components values. The performance of the proposed hybrid forecasting model is evaluated by using real data from National Renewable Energy Laboratory. The simulation results show that it can obtain better forecasting accuracy than some previously reported methods. [ABSTRACT FROM AUTHOR]

Droop control is a common method in the universal microgrid applications. Conventional droop control is unpractical for low‐voltage microgrid, where the line impedance among distributed generation units (DGs) is mainly resistive to generate the active and reactive power of DG is coupled. Besides, accurate reactive power sharing is not achieved due to the voltage deviation of DGs caused by disparate line impedance in microgrid. An improved droop control based on the virtual power source (VPS) and composite virtual impedance, which is constituted by a negative resistance and a negative inductance, is proposed for low‐voltage microgrid. The virtual negative resistance counteracts the line resistance to decouple power. The reactive power sharing accuracy based on the virtual negative inductance and VPSs control increases through analysing the line and virtual voltage drop. Finally, the fractional‐order PID controller is adopted to control the inverter voltage. The optimal controller parameters are obtained by the differential evolution algorithm. To verify the viability and availability of improved control strategy, simulations are carried out on MATLAB/Simulink. [ABSTRACT FROM AUTHOR]