scholarly journals Cooperative Rotor Position Estimation of Active Magnetic Bearings in Flywheel energy storage system with Unsaturated Magnetic Bias

2015 ◽  
Author(s):  
M. Tang ◽  
L.J. Ding ◽  
M. Li ◽  
H. Zhang ◽  
W. Wei ◽  
...  
Keyword(s):  
Energy Storage ◽  
Storage System ◽  
Energy Storage System ◽  
Position Estimation ◽  
Active Magnetic Bearings ◽  
Flywheel Energy Storage ◽  
Magnetic Bias ◽  
Flywheel Energy Storage System ◽  
Rotor Position ◽  
Rotor Position Estimation

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>

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

2017 ◽  
Vol 126 (1C) ◽  
pp. 57
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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