Friday , September 21 2018

Adaptive Control of Solid State Transformer Using Type-2 Fuzzy Neural System

Hakan ACIKGOZ1*, O. Fatih KECECIOGLU2, Israfil KARADOL1, Ahmet GANI2, Mustafa SEKKELI2

1 Kilis Aralik University,
Department of Electrical Science,
Kilis, 79000, Turkey.
hakanacikgoz@kilis.edu.tr (* Corresponding author),
israfilkaradol@kilis.edu.tr

2 K. Maras Sutcu Imam University,
Department of Electrical and Electronics Engineering,
K. Maras, 46100, Turkey.
fkececioglu@ksu.edu.tr, agani@ksu.edu.tr, msekkeli@ksu.edu.tr

ABSTRACT: Solid State Transformer (SST), considered as one of the emerging technologies, has a very important place in future electrical energy systems since it has many excellent features such as low volume/weight, controllability, active and reactive power control, voltage regulation, harmonic filtering, reactive power compensation. Considering all these superior features, it is inevitable that there are many designs and control strategies for SSTs. In recent years, many studies have been carried out for SSTs. These studies are generally based on control strategies and schemes. In this study, type-2 fuzzy neural system (T2FNS) which has nonlinear and robust structure has been proposed and investigated for SST. The mathematical models and control schemes of SST including input, isolation and output stages are explained in detail. Then, PI controller, type-1 fuzzy neural system (T1FNS) and T2FNS are designed to control three stages of SST. In order to investigate the dynamic performance of SST based on T2FNS, simulation studies have been realized under input voltage harmonics, unbalanced input voltages and voltage sag/swell conditions in MATLAB/Simulink environment.

KEYWORDS: Solid state transformers, Transformers, Type-1 fuzzy neural system, Type-2 fuzzy neural system.

>>FULL TEXT: PDF

CITE THIS PAPER AS:
Hakan Acikgoz, O. Fatih Kececioglu, Israfil Karadol, Ahmet Gani, Mustafa Sekkeli, Adaptive Control of Solid State Transformer Using Type-2 Fuzzy Neural System, Studies in Informatics and Control, ISSN 1220-1766, vol. 26(2), pp. 171-182, 2017.

https://doi.org/10.24846/v26i2y201705