The Controller Parameters Optimization for Droop Controlled Distributed Generators in Microgrid

Abstract-The microgrid is a typical cyber-physical microgrid system (CPMS). The physical unconventional distributed generators (DGs) are intermittent and inverter-interfaced which makes them very different to control. The cyber components, such as the embedded computer and communication network, are equipped with DGs, to process and transmit the necessary information for the controllers. In order to ensure system-wide observability, controllability and stabilization for the microgrid, the cyber and physical component need to be integrated. For the physical component of CPMS, the droop-control method is popular as it can be applied in both modes of operation to improve the grid transient performance. Traditional droop control methods have the drawback of the inherent trade-off between power sharing and voltage and frequency regulation. In this paper, the global information (such as the average voltage and the output active power of the microgrid and so on) are acquired distributedly based on multi-agent system (MAS). Based on the global information from cyber components of CPMS, automatic generation control (AGC) and automatic voltage control (AVC) are proposed to deal with the drawback of traditional droop control. Simulation studies in PSCAD demonstrate the effectiveness of the proposed control methods.Index Terms-Cyber-physical microgrid system (CPMS), microgrid, automatic generation control (AGC), multiagent system (MAS), droop control, global information discovery.

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“…Fig. 6 can be found in our previous work [36] . In this paper we only discuss the level of MAS based AGC.…”

Section: A Droop Controlmentioning

confidence: 78%

MAS based distributed automatic generation control for cyber-physical microgrid system

Yang

Cong

Shuang

et al. 2016

IEEE/CAA J. Autom. Sinica

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“…The PSO algorithm is implemented as follows [13]: 1) Initialize the system parameters, including particle velocity, maximum iteration number of particle position, position limits and so on, which are generated randomly.…”

Section: B Pso Algorithm Implementation Stepsmentioning

confidence: 99%

The rapid development of three-phase grid-forming micro-source inverter based on SMC and PI control

Bian

Zang

et al. 2017

2017 China International Electrical and Energy Conference (CIEEC)

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Abstract-The rapid development process of a proposed three-phase grid-forming micro-source inverter is presented in the paper. Sliding mode control (SMC) and PI control strategies are adopted synthetically to implement the dual closed-loop controller. In order to avoid the repeated selection of control parameters, the particle swarm optimization (PSO) algorithm is applied for parameters' optimization. Simulation results certificate the correctness of the proposed controller. And then, the automatic generation of control codes is realized based on the model design method. The actual hardware experiments corresponding to the simulation ones are presented in the paper. The results demonstrate that the inverter based on the rapid development is effective and the proposed controller performs better robustness property over conventional PI-based control strategy.

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Bounded Region Optimization of PID Gains for Grid Forming Inverters with Genetic Algorithms

Gutiérrez

Ponce

Molina

2019

Advances in Soft Computing

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The Controller Parameters Optimization for Droop Controlled Distributed Generators in Microgrid

Cited by 8 publications

References 14 publications

MAS based distributed automatic generation control for cyber-physical microgrid system

MAS based distributed automatic generation control for cyber-physical microgrid system

The rapid development of three-phase grid-forming micro-source inverter based on SMC and PI control

Bounded Region Optimization of PID Gains for Grid Forming Inverters with Genetic Algorithms

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