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A resilience-oriented bidirectional anfis framework for networked microgrid management

Afzal, Muhammad Zeshan, Aurangzeb, Muhammad, Iqbal, Sheeraz, Rehman, Atiq ur, Kotb, Hossam, AboRas, Kareem M., Elgamli, Elmazeg and Shouran, Mokhtar ORCID: 2022. A resilience-oriented bidirectional anfis framework for networked microgrid management. Processes 10 (12) , 2724. 10.3390/pr10122724

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This study implemented a bidirectional artificial neuro-fuzzy inference system (ANFIS) to solve the problem of system resilience in synchronized and islanded grid mode/operation (during normal operation and in the event of a catastrophic disaster, respectively). Included in this setup are photovoltaics, wind turbines, batteries, and smart load management. Solar panels, wind turbines, and battery-charging supercapacitors are just a few of the sustainable energy sources ANFIS coordinates. The first step in the process was the development of a mode-specific control algorithm to address the system’s current behavior. Relative ANFIS will take over to greatly boost resilience during times of crisis, power savings, and routine operations. A bidirectional converter connects the battery in order to keep the DC link stable and allow energy displacement due to changes in generation and consumption. When combined with the ANFIS algorithm, PV can be used to meet precise power needs. This means it can safeguard the battery from extreme conditions such as overcharging or discharging. The wind system is optimized for an island environment and will perform as designed. The efficiency of the system and the life of the batteries both improve. Improvements to the inverter’s functionality can be attributed to the use of synchronous reference frame transformation for control. Based on the available solar power, wind power, and system state of charge (SOC), the anticipated fuzzy rule-based ANFIS will take over. Furthermore, the synchronized grid was compared to ANFIS. The study uses MATLAB/Simulink to demonstrate the robustness of the system under test.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Engineering
Additional Information: License information from Publisher: LICENSE 1: URL:, Type: open-access
Publisher: MDPI
Date of First Compliant Deposit: 6 January 2023
Date of Acceptance: 14 December 2022
Last Modified: 10 Feb 2024 02:11

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