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Performance Enhancement of Grid Connected Multilevel Inverter Based Wind Energy Conversion System with LVRT Capability Using Optimized Type 2 ANFIS Based DVR

International Journal of Electrical and Electronics Engineering
© 2024 by SSRG - IJEEE Journal
Volume 11 Issue 10
Year of Publication : 2024
Authors : Ch. Sajan, P. Satish Kumar, Peter Virtic
10.14445/23488379/IJEEE-V11I10P124
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How to Cite?

Ch. Sajan, P. Satish Kumar, Peter Virtic, "Performance Enhancement of Grid Connected Multilevel Inverter Based Wind Energy Conversion System with LVRT Capability Using Optimized Type 2 ANFIS Based DVR," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 10, pp. 231-248, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I10P124

Abstract:

A Permanent Magnet Synchronous Generator (PMSG) based Wind Energy Conversion System (WECS) holds significant importance in the realm of Renewable Energy Sources (RES) for several reasons. The permanent magnets in the generator eliminate the need for a separate excitation system, leading to improved efficiency in power conversion. This makes PMSG-based WECS an effective and reliable source of wind energy electricity. The motivation behind the proposed conceptual framework stems from the need to overcome the limitations related to the integration of RES into the power grid, specifically focusing on voltage stability and Low Voltage Ride Through (LVRT) capability of PMSG based WECS. A Dynamic Voltage Restorer (DVR), empowered by an energy storage device, is used to mitigate voltage fluctuations and disturbances. The input DC voltage to the DVR is intricately regulated by a Type 2 Adaptive Neuro Fuzzy Inference System (ANFIS) Controller optimized using the Seagull algorithm, exhibiting intelligent adaptability to dynamic conditions. The rectified output from the WECS transforms an Isolated Flyback converter. Subsequently, a 31-Level Cascaded H-Bridge Multilevel Inverter (CHBMLI) along with a Proportional-Integral (PI) controller aids in generating high-quality AC output. By addressing challenges related to voltage stability and the ability to ride through low-voltage conditions, the proposed work contributes to enhanced grid stability. The use of advanced control techniques, including the Type 2 ANFIS Controller optimized by the Seagull algorithm, adds a layer of intelligent adaptability to changing environmental and grid conditions. A lower Total Harmonic Distortion (THD) Value of 1.29% is shown during the validation of the created system utilizing MATLAB/Simulink, assuring significant LVRT capabilities.

Keywords:

PMSG, WECS, RES, Low Voltage Ride Through (LVRT), Type 2 Adaptive Neuro Fuzzy Inference System,31-Level CHBMLI, PI.

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