The dynamic model of electrical aircraft system feeding a buck-boost converter

Chayinthu, Wiphusana; Areerak, Kongpol

doi:10.1109/ieecon.2017.8075734

Cited by 1 publication

(1 citation statement)

References 5 publications

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“…Various dynamic models of EAC have been introduced in the literature [39][40][41][42][43][44][45]. Most of these models focus on aerodynamics, converter modelling, or EAC flight modelling.…”

Section: Electric Aircraft Modelmentioning

confidence: 99%

Robust self‐adaptive fuzzy controller for load‐frequency control of islanded airport microgrids considering electric aircraft energy storage and demand response

Shayeghi,

Davoudkhani,

Bizon

2024

IET Renewable Power Gen

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The rise of the renewable energy sources (RES) in microgrids has increased the impact of damping and low inertia on network stability. This gives rise to several issues in the power systems, including power fluctuations from variable RES, degradation of the frequency regulation, voltage surges, and oversupply from grid‐connected distributed generators. This paper presents a cascaded hybrid load‐frequency controller for an airport microgrid that addresses frequency fluctuations caused by load changes. It is combining a self‐adaptive fuzzy controller (SAF) and a classical two‐degree‐of‐freedom proportional integral derivative (PID) controller (2DOFPID) called CSAF‐2DOFPID. It is applied to the control section of a diesel generator, an electric aircraft energy storage system, and a demand response program. The controller parameters are optimized using a suggested hybrid differential artificial hummingbird algorithm (D‐AHA) to minimize frequency deviation. The proposed control framework is simulated on an airport microgrid comprising various components, and the impact of time delay on the controller's signals, system uncertainty is also examined. The simulation results demonstrate that the proposed self‐adaptive cascaded fuzzy controller outperforms other controllers such as PID, CF‐PID, and CF‐2DOFPID in reducing frequency fluctuations, and the D‐AHA algorithm achieves favourable results compared to other algorithms in the optimal tuning controller parameters.

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