Shape-Adaptive RBF Neural Network for Model-Based Nonlinear Controlling Applications

Kumara, K.; Dilhani, M H M R Shyamali

doi:10.1007/978-981-33-4305-4_47

Inventive Computation and Information Technologies

2021

DOI: 10.1007/978-981-33-4305-4_47

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Shape-Adaptive RBF Neural Network for Model-Based Nonlinear Controlling Applications

K. Kumara

M H M R Shyamali Dilhani

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2021

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Design of Inverter Voltage Mode Controller by Backstepping Technique for Nonlinear Power System Model

Karuppusamy¹

2021

JEEA

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Modelling systems are a new sort of electrical network that can be easily adapted. Dispersed generators are linked to a microgrid using voltage source inverters. Nonlinear modelling systems are used in this study to create an inverter voltage mode controller for power systems to control power supply volatility. Controller for a nonlinear inverter that operates in voltage control mode is proposed. The primary goal is to ensure that the output voltage of the system matches a predetermined standard. Once the system model is completed, the controller is constructed using the backstepping method. After the control law is developed, several simulations are run to test the proposed controller's performance. According to simulation findings and formal analysis, the output voltage matches the reference voltage with global asymptotic stability. The accomplishment of this work is that the controller built, works in both grid-connected and inverter voltage modes of microgrid operation.

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Design of Inverter Voltage Mode Controller by Backstepping Technique for Nonlinear Power System Model

Karuppusamy¹

2021

JEEA

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Shape-Adaptive RBF Neural Network for Model-Based Nonlinear Controlling Applications

Cited by 1 publication

References 17 publications

Design of Inverter Voltage Mode Controller by Backstepping Technique for Nonlinear Power System Model

Design of Inverter Voltage Mode Controller by Backstepping Technique for Nonlinear Power System Model

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