Cyber-physical cooperative control strategy for islanded micro-grid considering communication interruption

Abstract

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.

A Comprehensive Review of the State-of-the-Art of Secondary Control Strategies for Microgrids

“…Along with the disruptions produced via communication system, cyber-attacks may disturb performance of MG in such a means that MG may fail, which is discussed in [155] through Software Defined Networking (SDN). Communication system failure is one of the turbulences that affect behavior of MG controller, that is discussed in [156], the proposed method for modernization of the lines of communication. The Table 10 presents control procedures utilized in existence of the communication failures.…”

A hybrid prediction-based microgrid energy management strategy considering demand-side response and data interruption

“…In the beginning of optimization, the method of roulette is used to select the initial particle, and then crossover and mutation are taken to expand the searching range of the algorithm (i.e., prevent it being trapped into local optimal solution).The convergence speed is improved by the method of elite selection [37]. The flow chart of the algorithm is shown in Fig.…”

“…, where 0.5<constant<1; F is the total operating cost of MG. According to the fitness function value of each particle, roulette selection is carried out [37]. This scheme can expand the searching range of PSO.…”

Event‐triggered hybrid control strategy based on hybrid automata and decision tree for microgrid

“…Affected by changing environmental factors and load demands, DERs often need to switch modes of operation [5], thus exhibiting discrete behaviour. In addition, DERs as electrical equipment need to follow the corresponding electrical laws and thus exhibit dynamic continuous behaviour. In order to cope with unexpected situations, each unit in the microgrid system often needs to operate cooperatively [6, 7], which puts higher requirements on the control strategy. For the microgrid system, in addition to the above‐mentioned logical switching problem, it should also consider the problem that the voltage, power, and other indicators can continuously change according to requirements, i.e. continuous dynamic adjustment problem. When multi‐model switching is performed, the number of switching will have an impact on system stability.…”

“…In order to cope with unexpected situations, each unit in the microgrid system often needs to operate cooperatively [6, 7], which puts higher requirements on the control strategy.…”