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

Dou, Chunxia; Teng, Shengqi; Zhang, Tengfei; Zhang, Bo; Ma, Kai

doi:10.1049/iet-gtd.2018.6928

Cited by 3 publications

(1 citation statement)

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“…In these models, the number of discrete states grows exponentially with the number of processes, which is an obstacle in developing highly flexible and adaptive supervisory control systems [19]. To reduce the complexity, modular modelling techniques can be used to develop an FSA model for each module as well as designing a distributed supervisory control system or use a coordinator agent to manage the appropriate coordination among them [20,21].…”

Section: Introductionmentioning

confidence: 99%

A distributed rule‐based power management strategy in a photovoltaic/hybrid energy storage based on an active compensation filtering technique

Abadi

Bidram

2021

IET Renewable Power Gen

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This paper proposes a distributed rule-based power management strategy for dynamic power balancing and power smoothing in a photovoltaic (PV)/battery-supercapacitor hybrid energy storage system. The system contains a PV system, a battery-supercapacitor hybrid energy storage system (HESS), and a group of loads. Firstly, an active compensation technique is proposed which improves the efficiency of the power smoothing filter. Then, a distributed supervisory control technique is employed that prevents the BESS and SC from SOC violation while maintaining the balance between generation and load. To this end, the system components are divided into three different reactive agents including an HESS agent, a PV agent, and a load agent. These agents react to the system changes by switching their operational mode upon satisfying a predefined rule. To analyse the hybrid dynamical behaviour of the agents and design the supervisory controllers, the agents are modelled in hybrid automata frameworks. It is shown that the proposed distributed approach reduces the complexity of the supervisory control system and increases its scalability compared to its equivalent centralized method. Finally, the performance of the proposed approach is validated using a test system simulated in MATLAB/Simulink.

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Section: Introductionmentioning

confidence: 99%