Fuzzy logic control for a speed of a flywheel energy storage system associated the wind generator

2011 ◽  
Author(s):  
I. Hamzaoui ◽  
F. Bouchafaa ◽  
A. Talha ◽  
A. Boukhlifa
Keyword(s):  
Fuzzy Logic ◽  
Energy Storage ◽  
Fuzzy Logic Control ◽  
Storage System ◽  
Energy Storage System ◽  
Flywheel Energy Storage ◽  
Flywheel Energy Storage System ◽  
Wind Generator ◽  
Logic Control

scholarly journals Intelligent control of flywheel energy storage system associated with the wind generator for uninterrupted power supply

2020 ◽  
Vol 11 (4) ◽  
pp. 2062
Author(s):  
Bensaid Amel ◽  
Zebirate Soraya ◽  
Chaker Abdelkader
Keyword(s):  
Energy Storage ◽  
Control Strategies ◽  
Storage System ◽  
Induction Machine ◽  
Energy Storage System ◽  
Flywheel Energy Storage ◽  
Flywheel Energy Storage System ◽  
Wind Generator ◽  
Logic Control ◽  
Comparative Results

Wind energy is currently the fastest-growing energy source in the world. However, the inherent characteristic of intermittent energy production, due to the stochastic nature of wind, still comprises the main drawback of wind power. To avoid such problems, various configurations have been reccomended in order to reduce output power variation. The paper concentrates on performance benefits of adding energy storage system with the wind generator in order to regulate the electric power delivered into the power grid. Compared with other means of energy storage, the flywheel energy storage system (FESS) is the best choice to solve power quality problems. In this paper, a FESS associated to a variable speed wind generation (VSWG) is investigated by presenting two control strategies applied to the storage system equipped with an induction machine; both techniques are studied and developed and consist of a field control (FOC) and a Fuzzy Logic Control (FLC). Simulation model is established in MATLAB/Simulink and comparative results are then reported.

scholarly journals Fuzzy Logic Control of a Battery Energy Storage System for Stability Improvement in an Islanded Microgrid

2018 ◽  
Vol 10 (5) ◽  
pp. 1645 ◽  
Author(s):  
Naowarat Tephiruk ◽  
Weerawoot Kanokbannakorn ◽  
Thongchart Kerdphol ◽  
Yasunori Mitani ◽  
Komsan Hongesombut
Keyword(s):  
Fuzzy Logic ◽  
Energy Storage ◽  
Fuzzy Logic Control ◽  
Storage System ◽  
Energy Storage System ◽  
Battery Energy Storage System ◽  
Battery Energy Storage ◽  
Logic Control ◽  
Islanded Microgrid ◽  
Battery Energy

ANFIS CONTROLLER FOR AN ACTIVE MAGNETIC BEARINGS TO SUSPEND A FLYWHEEL ENERGY STORAGE SYSTEM

2017 ◽  
Vol 126 (2B) ◽  
Author(s):  
Nguyen Van Sum
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Energy Storage ◽  
High Speed ◽  
Storage System ◽  
Energy Storage System ◽  
Active Magnetic Bearings ◽  
Control Performance ◽  
Flywheel Energy Storage ◽  
Flywheel Energy Storage System

<p><span style="font-family: Times New Roman;">A flywheel energy storage system (FESS) is an effective energy-saving device. It works by accelerating a rotor flywheel disc at a very high speed and maintaining the energy in the system as rotational energy. Active magnetic bearings (AMBs) are ideally suited for use at high-speed and are so used in FESSs. This work develops a mathematical model of the electromagnet force and rotor dynamic of a flywheel. The systems for controlling the position and velocity of the flywheel are designed based on the emerging approaches of an adaptive neuro-fuzzy inference system (ANFIS). Fuzzy logic has occurred as a mathematical tool to deal with the uncertainties in human perception. It also provides a framework for applying approximate human reasoning capabilities to knowledge-based systems. Additionally, ANFIS has emerged as an intelligent controller with learning and adaptive capabilities. ANFIS is combined the fuzzy logic controller (FLC) and neural networks (NNs). In the method that is developed herein, the control model uses Takagi-Sugeno fuzzy logic, in which the back-propagation algorithm processes information from neural networks to adjust suitably the parameters of the fuzzy controller, and the output control signal tracks the input signal. This method can be applied to improve the control performance of nonlinear systems. The output signal responses transient performance of systems use an ANFIS that must be trained through a learning process to yield suitable membership functions and weightings. The results of the FESS indicated that the system responds with satisfactory control performance to reduce overshoot, a zero-error steady-state, and short rise time. The proposed controller can be feasibly applied to FESS with various external disturbances, and the effectiveness of the ANFIS with self-learning and self-improving capacities is proven.</span></p>

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