Paper Info
Title
A Hierarchical Genetic Optimization of a Fuzzy Logic System for Flow Control in Micro Grids.
Abstract
Computational Intelligence techniques are today widely used to solve complex engineering problems. Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems are nowadays adopted as hybrids techniques in the commercial and industrial environment. In this paper, we present an interesting application of the FUZZY-GA paradigm to Smart Grids. In particular, this study focuses on the possibility of tuning a Fuzzy Rule Base trying to discover, by means of a GA, a minimal fuzzy rules set in a Fuzzy Logic Controller (FLC) adopted to perform decision making for the power flow management task in a microgrid. The RB optimization is obtained through Hierarchical Genetic Algorithm, based on an encoding scheme inspired by Nature, applied to the optimization of the FIS parameters. Tests show how the proposed controller scheme is effective in maximizing the economic return when dealing with the problem of power flows management in a microgrid, equipped with an energy storage system. keywords: Microgrid, Power Flow Optimization, Battery Management, Fuzzy Systems, Genetic Algorithms.
Year
Venue
Field
2016
arXiv: Artificial Intelligence
Computational intelligence,Computer science,Fuzzy logic,Artificial intelligence,Adaptive neuro fuzzy inference system,Fuzzy control system,Fuzzy number,Machine learning,Microgrid,Genetic algorithm,Fuzzy rule
DocType
Volume
Citations 
Journal
abs/1604.04789
0
PageRank 
References 
Authors
0.34
26
3
Name
Order
Citations
PageRank
Enrico De Santis1505.92
Alireza Sadeghian226925.59
Antonello Rizzi336341.68