Modeling and Design of a Grid-Tied Renewable Energy System Exploiting Re-Lift Luo Converter and RNN Based Energy Management

The significance of the Hybrid Renewable Energy System (HRES) is profound in the current scenario owing to the mounting energy requirements, pressing ecological concerns and the pursuit of transitioning to greener energy alternatives. Thereby, the modeling and design of HRES, encompassing PV–WECS–Battery, which mainly focuses on efficient power conversion and advanced control strategy, is proposed. The voltage gain of the PV system is improved using the Re-lift Luo converter, which offers high efficiency and power density with minimized ripples and power losses. Its voltage lift technique mitigates parasitic effects and delivers improved output voltage for grid synchronization. To control and stabilize the converter output, a Proportional–Integral (PI) controller tuned using a novel hybrid algorithm combining Grey Wolf Optimization (GWO) with Hermit Crab Optimization (HCO) is implemented. GWO follows the hunting and leadership characteristics of grey wolves for improved simplicity and robustness. By simulating the shell selection behavior of hermit crabs, the HCO adds diversity to exploitation. Due to these features, the hybrid GWO–HCO algorithm enhances the PI controller’s capability of handling dynamic non-linear systems, generating better control accuracy, and rapid convergence to optimal solutions. Considering the Wind Energy Conversion System (WECS), the PI controller assures improved stability despite fluctuations in wind. A Recurrent Neural Network (RNN)-based battery management system is also incorporated for accurate monitoring and control of the State of Charge (SoC) and the terminal voltage of battery storage. The simulation is conducted in MATLAB Simulink 2021a, and a lab-scale prototype is implemented for real-time validation. The Re-lift Luo converter achieves an efficiency of 97.5% and a voltage gain of 1:10 with reduced oscillations and faster settling time using a Hybrid GWO–HCO–PI controller. Moreover, the THD is reduced to 1.16%, which indicates high power quality and reduced harmonics.

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Journal
Sustainability
Published
2024-12-30
DOI
https://doi.org/10.3390/su17010187
Citations
8
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
2.63
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article

Modeling and Design of a Grid-Tied Renewable Energy System Exploiting Re-Lift Luo Converter and RNN Based Energy Management

Subha Karuvelam Subha Karuvelam, Kavitha Paulsamy
8 citations
Sustainability
Microgrid Control and Optimization
2.63
article

Modeling and Design of a Grid-Tied Renewable Energy System Exploiting Re-Lift Luo Converter and RNN Based Energy Management

Subha Karuvelam Subha Karuvelam, Kavitha Paulsamy
article en
8 citations

Abstract

The significance of the Hybrid Renewable Energy System (HRES) is profound in the current scenario owing to the mounting energy requirements, pressing ecological concerns and the pursuit of transitioning to greener energy alternatives. Thereby, the modeling and design of HRES, encompassing PV–WECS–Battery, which mainly focuses on efficient power conversion and advanced control strategy, is proposed. The voltage gain of the PV system is improved using the Re-lift Luo converter, which offers high efficiency and power density with minimized ripples and power losses. Its voltage lift technique mitigates parasitic effects and delivers improved output voltage for grid synchronization. To control and stabilize the converter output, a Proportional–Integral (PI) controller tuned using a novel hybrid algorithm combining Grey Wolf Optimization (GWO) with Hermit Crab Optimization (HCO) is implemented. GWO follows the hunting and leadership characteristics of grey wolves for improved simplicity and robustness. By simulating the shell selection behavior of hermit crabs, the HCO adds diversity to exploitation. Due to these features, the hybrid GWO–HCO algorithm enhances the PI controller’s capability of handling dynamic non-linear systems, generating better control accuracy, and rapid convergence to optimal solutions. Considering the Wind Energy Conversion System (WECS), the PI controller assures improved stability despite fluctuations in wind. A Recurrent Neural Network (RNN)-based battery management system is also incorporated for accurate monitoring and control of the State of Charge (SoC) and the terminal voltage of battery storage. The simulation is conducted in MATLAB Simulink 2021a, and a lab-scale prototype is implemented for real-time validation. The Re-lift Luo converter achieves an efficiency of 97.5% and a voltage gain of 1:10 with reduced oscillations and faster settling time using a Hybrid GWO–HCO–PI controller. Moreover, the THD is reduced to 1.16%, which indicates high power quality and reduced harmonics.

SustainabilityVol. 17(1)
Government College of Engineering, Tirunelveli (IN)
Affordable and clean energy
Openalex Percentile: Top 9%
Microgrid Control and Optimization
2.63
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Modeling and Design of a Grid-Tied Renewable Energy System Exploiting Re-Lift Luo Converter and RNN Based Energy Management — Subha Karuvelam Subha Karuvelam, Kavitha Paulsamy · Sustainability (2024) | TGRS Research Map | TGRS