A Resilience-Oriented Bidirectional ANFIS Framework for Networked Microgrid Management

Abstract

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.

Digital Transformation of Microgrids: A Review of Design, Operation, Optimization, and Cybersecurity

“…MPC uses mathematical models of the microgrid and predictive algorithms to optimize the system's operation [107,108]. Fuzzy Logic Control: Fuzzy logic control is a technique used to optimize the operation of a microgrid by defining rules that describe the relationship between inputs and outputs [109].…”

“…MEMSs can also coordinate with other systems, such as local utility companies, to ensure that the microgrid has access to backup power supplies if needed. This helps to ensure that the microgrid can continue to provide energy even during emergencies or disasters [109,136].…”

Multiple Grid-Connected Microgrids with Distributed Generators Energy Sources Voltage Control in Radial Distribution Network Using ANFIS to Enhance Energy Management

“…In article [22], the authors present a Resilience-Oriented Bidirectional ANFIS Framework for connected Microgrid Management systems with Renewable Energy Sources such as Photovoltaic, Wind Turbines, Batteries, and Intelligent Load Control, while electrical networks that are integrated with storage power sources, voltage consistency in the integrated networks with optimal battery charge management controllerbased hybrid GA-ANFS for PV-wind microgrid was presented in this paper [23]. In [24], the authors implemented a switching mechanism using the Emperor penguin based on an adaptive fuzzy neuro inference system (EP-ANFIS) controller to ensure efficient storage unit control in a Microgrid with PV-WT System, and Smart battery controller using ANFIS [25].…”