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Optimized PI Controller Based Reboost Luo Converter for Micro Grid Application

R. K. Negesh1,*, S. Karthikeyan2

1 Marthandam College of Engineering and Technology, Kuttakuzhi, 629177, India
2 KSR College of Engineering, Thiruchengode, 637215, India

* Corresponding Author: R. K. Negesh. Email: email

Intelligent Automation & Soft Computing 2023, 36(1), 1151-1172. https://doi.org/10.32604/iasc.2023.027764

Abstract

In the recent era, the significance of Renewable Energy (RE) sources in the process of power generation has attained considerable attention as it provides multiple beneficial impacts without harming the environment. The Photovoltaic (PV) and Doubly-Fed Induction Generator (DFIG) fed wind turbine are employed as hybrid power sources in this study. The output voltages of these sources are independently controlled by separate controllers in an optimal manner for effectively maximizing the overall performance of the system. The Reboost Luo converter is introduced in this work to maximize the output voltage of PV in an efficient manner whereas the Grey Wolf Optimization (GWO) tuned Proportional Integral (PI) is used to optimize the converter output. The wind turbine output is fed to rectifier that is controlled by a PI controller. Furthermore, a battery setup is utilized with bidirectional converter as a secondary power source to ensure continuous power supply to the grid. An intelligent Adaptive Neuro-Fuzzy Inference System (ANFIS) is implemented to regulate the power flow in two directions between the battery and Point of Common Coupling (PCC). The output of PV, Wind and Battery are combined at a single point and fed to the grid through a three phase inverter. The overall setup is validated through MATLAB Simulink and the performances are verified with the developed experimental prototype.

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Cite This Article

R. K. Negesh and S. Karthikeyan, "Optimized pi controller based reboost luo converter for micro grid application," Intelligent Automation & Soft Computing, vol. 36, no.1, pp. 1151–1172, 2023.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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