Fuzzy Approach for Managing Renewable Energy Flows for DC-Microgrid with Composite PV-WT Generators and Energy Storage System

Abstract

Recently, the implementation of software/hardware systems based on advanced artificial intelligence techniques for continuous monitoring of the electrical parameters of intelligent networks aimed at managing and controlling energy consumption has been of great interest. The contribution of this paper, starting from a recently studied DC-MG, fits into this context by proposing an intuitionistic fuzzy Takagi–Sugeno approach optimized for the energy management of isolated direct current microgrid systems consisting of a photovoltaic and a wind source. Furthermore, a lead-acid battery guarantees the stability of the DC bus while a hydrogen cell ensures the reliability of the system by avoiding blackout conditions and increasing interaction with the loads. The fuzzy rule bank, initially built using the expert’s knowledge, is optimized with the aforementioned procedure, maximizing external energy and minimizing consumption. The complete scheme, modeled using MatLab/Simulink, highlighted performance comparable to fuzzy Takagi–Sugeno systems optimized using a hybrid approach based on particle swarm optimization (to structure the antecedents of the rules) and minimum batch squares (to optimize the output).

Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

“…Because they are straightforward and clear, traditional rule-based control techniques have been extensively employed in MG management systems. Nevertheless, these techniques often have difficulty adjusting to dynamic and uncertain operating conditions, which restricts their use in maximizing system performance [8][9][10].…”

EEG Data Analysis Techniques for Precision Removal and Enhanced Alzheimer’s Diagnosis: Focusing on Fuzzy and Intuitionistic Fuzzy Logic Techniques

“…The introduction of methodologies based on fuzzy logic, in the field of AI, represents a significant advancement in the removal of artifacts from EEG signals. This approach, inspired by the way human reasoning manages ambiguous or incomplete information, is particularly effective in managing the uncertainty and imprecision typical of biological signals, proving essential to overcoming the challenges related to the intrinsic variability of EEG data [58][59][60][61][62][63][64][65]. The use of fuzzy techniques is motivated by their effectiveness in managing the complexity of neurological signals, significantly improving the ability to differentiate between relevant brain activity and artifactual distortions [66][67][68][69][70].…”