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Summary: In this paper, a cyber‐physical cooperative control strategy considering communication interruption is proposed to improve the control effect on output voltages and frequency obtained by droop control of distributed energy resources (DERs). The control structure consists of the physical and cyber layers. And in the two layers, the interruption occurs mostly in sensors and data aggregators. To solve the problem of interruption among data aggregators, a path‐planning method combined with improved Dijkstra algorithm (IDA) and improved genetic algorithm (IGA) is proposed in the cyber layer. To solve the problem of interruption among sensors, a predictive compensation method combined with extreme learning machine (ELM) and model predictive control (MPC) is proposed in the physical layer. Meanwhile, so as to improve the control effect of DERs, a virtual leader‐following consensus control (VLFCC) method combined with double sliding mode control (SMC) is first proposed in cyber layer and then applied to the physical layer to accomplish the secondary control on DERs. Finally, the simulation results show that under the condition of communication interruption, the output voltage and frequency of each DER can be adjusted to their respective reference values through the proposed cooperative control strategy. [ABSTRACT FROM AUTHOR]

At present, the traditional islanded microgrid is changing to a cyber-physical system. And the data in its cyber layer is often used to solve practical problems in the physical layer through sensors and communicators. But the use of communication data also brings a lot of problems. Among them, the upload interruption problem (i.e., communicator or sensor failure) will make a heavy impact on the system. To improve the voltage and frequency stability of microgrid through secondary control considering data uploading interruption, an improved growing and pruning-radial basis function neural network-based secondary control strategy is designed in this study. The main designs can be summarized as below: (1) When the communicator cannot receive data, a prediction compensation part is designed directly in the corresponding communicator, i.e., the predicted data is used to complete the follow-up works; (2) When the sensor cannot send data, a nonlinear cyber-physical vulnerability assessment part and a neighbor transmission part are designed. In this way, a neighbor sensor is selected to send the interrupted data for the follow-up control process; (3) Based on these designs above, a novel secondary control is designed for microgrid. Finally, the simulation results show the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

• A CPHVA is proposed to evaluate the vulnerability of nodes in islanded MG. • The interruption problem in communicators is solved by path reconstruction. • The interruption problem in sensors is solved by predictive compensation. • A novel secondary control method is proposed to improve the control effect on DERs. Recently, more and more consensus-based hierarchical control methods are proposed in micro-grid. The main components of these methods are primary and secondary controllers. And they are usually used to adjust the output frequency and voltages of distributed energy resources. But the implementation of these methods is highly dependent on the communication network which is consisted by sensors and communicators. Once the data transmit of some sensors or communicators is interrupted, it will directly affect the control effect of hierarchical control. To solve the above problems, a cyber-physical cooperative response strategy is proposed in this paper. The main designs are as follows: (1) To solve the interruption problem among communicators, there are two defense methods proposed in the cyber layer: path planning and path reconstruction. Both methods are combined with cyber-physical hybrid vulnerability assessment and improved genetic algorithm; (2) To solve the interruption problem in the output channels of sensors, a distributed predictive compensation method combined with extreme learning machine and model predictive control is proposed in physical layer; (3) Based on the designs in (1) and (2), a novel secondary controller is proposed in this paper. Finally, the simulation results confirm the effectiveness of the proposed cooperative response strategy. [ABSTRACT FROM AUTHOR]