FOSMC Control Mechanism For Solar and Battery based Microgrid System

Shaik, Silar Sahib; Gudey, Satish Kumar

doi:10.1109/icepe50861.2021.9404515

Cited by 2 publications

(1 citation statement)

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“…However, using MPC to operate a 400 Hz GPU remains difficult owing to the inadequate ratio between the switching and output frequencies as well as the instability introduced by the input filter. A fractional-order sliding mode controller (FOSMC) can obtain a good tracking performance, which was proven in [18][19][20][21]. Although an FOSMC offers good performance, it requires the adjustment of many parameters and can only be achieved if the device parameters are constant, which is difficult to achieve in real life.…”

Section: Introductionmentioning

confidence: 99%

RBF Neural Network Fractional-Order Sliding Mode Control with an Application to Direct a Three Matrix Converter under an Unbalanced Grid

Yang

Fang

et al. 2022

Sustainability

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This paper presents a fractional-order sliding mode control scheme based on an RBF neural network (RBFFOSMC) for a direct three matrix converter (DTMC) operating under unbalanced grid voltages. The RBF neural network (RBF NN) is designed to approximate a nonlinear fractional-order sliding mode controller. The proposed method aims to achieve constant active power whilst maintaining a near unity input power factor. First, an opportune reference current is accurately generated according to the reference power and the RBFFOSMC is designed in a dq reference frame to achieve a perfect tracking of the input current reference. An almost constant active power, free of low-frequency ripples, is then supplied from the grid after compensating for the output voltage. Simulation and experimental studies prove the feasibility and effectiveness of the proposed control method.

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Section: Introductionmentioning

confidence: 99%