Cascaded controller for a standalone microgrid‐connected inverter based on triple‐action controller and particle swarm optimisation

Salim, Omar; Aboraya, Amgad; Arafa, Shawky I.

doi:10.1049/iet-gtd.2019.1641

Cited by 9 publications

(1 citation statement)

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“…In [30] Grasshopper Optimization Algorithm (GOA) was used, and in [31] PSO was used to tune the PI controller parameters. PSO was also used in [32] to design triple-action controllers' parameters for an islanded AC MG. In [33], GA was used to design controller parameters in the secondary control level of an AC MG, and in [34] a non-dominated sorting genetic algorithm-II was used to design the parameters for the fractional order PID controller.…”

Section: Introductionmentioning

confidence: 99%

Control Design and Parameter Tuning for Islanded Microgrids by Combining Different Optimization Algorithms

et al. 2022

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Load and supply parameters may be uncertain in microgrids (MGs) due for instance to the intermittent nature of renewable energy sources among others. Guaranteeing reliable and stable MGs despite parameter uncertainties is crucial for their correct operation. Their stability and dynamical features are directly related to the controllers’ parameters and power-sharing coefficients. Hence, to maintain power good quality within the desirable range of system parameters and to have a satisfactory response to sudden load changes, careful selection of the controllers and power-sharing coefficients are necessary. In this paper, a simple design approach for the optimal design of controllers’ parameters is presented in an islanded MG. To that aim, an optimization problem is formulated based on a small-signal state-space model and solved by three different optimization techniques including particle swarm optimization (PSO), genetic algorithm (GA), and a proposed approach based on the combination of both PSO and GA. The optimized coefficients are selected to guarantee desirable static and dynamic responses in a wide range of operations regardless of the number of inverters, system configuration, output impedance differences, and load types. Through the proposed design and tuning method, the performance of the MG is improved as compared to those obtained using state-of-art techniques. This fact is demonstrated by using numerical simulations performed on a detailed model implemented in PSIM© software.

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