scholarly journals Fuzzy Logic Control Contribution to the Rotor Speed Control of the Doubly Fed Induction Generator

2020 ◽  
Vol 9 (4) ◽  
pp. 540-546
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Induction Machine ◽  
Energy System ◽  
Pi Controller ◽  
Rotor Speed ◽  
Logic Control ◽  
Doubly Fed Induction Machine ◽  
Fuzzy Pi Controller ◽  
Doubly Fed

This paper presents the control of the wind energy system (WES), based on the doubly fed induction machine in generating mode (DFIG) using Artificial Intelligence (AI) technique based on fuzzy logic control. The performances of the conventional Integral Proportional (PI) and Fuzzy-PI controllers are compared with respect to the speed variation of the generator. The objective of the control strategy based on Fuzzy-PI controller is to ensure perfect tracking of the rotor speed and control the current regulation loops. The performances of the fuzzy-PI controller are studied via simulation on the Matlab/Simulink. We obtained perfect results that show the value of using a fuzzy PI controller

Type-2 Fuzzy Logic Control of a Doubly-Fed Induction Machine (DFIM)

2015 ◽  
Vol 4 (4) ◽  
pp. 139 ◽  
Author(s):  
Loukal Keltoum ◽  
Benalia Leila
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Induction Machine ◽  
Operating Conditions ◽  
Logic Control ◽  
Doubly Fed Induction Machine ◽  
Doubly Fed ◽  
Interval Type

The fuzzy controllers have demonstrated their effectiveness in the control of nonlinear systems, and in many cases have established their robust and that their performance is less sensitive to parameter variations over conventional controllers. In this paper, Interval Type-2 Fuzzy Logic Controller (IT2FLC) method is proposed for controlling the speed with a direct stator flux orientation control of doubly-fed induction motor (DFIM), we made a comparison between the Type-1 Fuzzy Logic Control (T1FLC) and IT2FLC of the DFIM, first a modeling of DFIM is expressed in a (d-q) synchronous rotating frame. After the development and the synthesis of a stabilizing control laws design based on IT2FLC. We use this last approach to the control of the DFIM under different operating conditions such as load torque and in the presence of parameter variation. The obtained simulation results show the feasibility and the effectiveness of the suggested method.

Self-Tuning Fuzzy Based PI Controller for DFIM Powered by Two Matrix Converters

2016 ◽  
Vol 7 (3) ◽  
pp. 665
Author(s):  
Abdelhakim Alalei ◽  
Abdeldjebar Hazzab ◽  
Ali Nesba
Keyword(s):  
Fuzzy Logic ◽  
Induction Machine ◽  
Pi Controller ◽  
Operating Conditions ◽  
Control Technique ◽  
Matrix Converters ◽  
The Matrix ◽  
Doubly Fed Induction Machine ◽  
Self Tuning ◽  
Doubly Fed

This paper presents a study of the Doubly Fed Induction Machine (DFIM) powered by two matrix converters; one connected to the stator windings and the other connected to the rotor windings. First, the mathematical model of DFIM and those of the matrix converters are developed. Then, the vector control technique is applied to the DFIM. Fuzzy logic is used in order to automatically adjust the parameters of the PI controller. The performance of this structure under different operating conditions is studied. Particular interest is given to the robustness of the fuzzy logic based control. The operation of the DFIM under overload conditions is also examined.  Simulation results obtained in MATLAB/Simulink environment are presented and discussed.

scholarly journals STATCOM with Fuzzy-PI Controller for Low Voltage-Ride through Enhancement of DFIG based Wind Farm

2021 ◽  
pp. 518-523
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Wind Farm ◽  
Reactive Power ◽  
Low Voltage ◽  
Pi Controller ◽  
Real Power ◽  
Low Voltage Ride Through ◽  
Logic Control ◽  
Fuzzy Pi Controller

This paper represents the study of Fuzzy Logic Control-Based STATCOM devices to enhance the capabilities of the low voltage-ride through of a grid connected DFIG based wind farm and its comparison with conventional STATCOM device. The proposed FLC based STATCOM device have designed using MATLAB/Simulink.Compare to the output responses of the voltage, currents, speed, real power and reactive power of both stator as well as rotor of the two investigated systems during grid faults, the STATCOM with fuzzy-PI controller gives best performance with faster recovery, adjustable speed, smoother current profiles and better reliable than traditional controller

scholarly journals Techno-Economic Analysis of a Fuzzy Logic Control Based Hybrid Renewable Energy System to Power a University Campus in Japan

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1960
Author(s):  
Tatsuya Hinokuma ◽  
Hooman Farzaneh ◽  
Ayas Shaqour
Keyword(s):  
Fuzzy Logic ◽  
Renewable Energy ◽  
Economic Analysis ◽  
Fuzzy Logic Control ◽  
Energy System ◽  
Levelized Cost Of Electricity ◽  
Logic Control ◽  
Hybrid Renewable Energy System ◽  
Renewable Energy System ◽  
Hybrid Renewable Energy

In order to reduce the load demand of buildings in Japan, this study proposes a grid-tied hybrid solar–wind–hydrogen system that is equipped with a maximum power point tracking (MPPT) system, using a fuzzy logic control (FLC) algorithm. Compared with the existing MPPTs, the proposed MPPT provides rapid power control with small oscillations. The dynamic simulation of the proposed hybrid renewable energy system (HRES) was performed in MATLAB-Simulink, and the model results were validated using an experimental setup installed in the Chikushi campus, Kyushu University, Japan. The techno-economic analysis (TEA) of the proposed system was performed to estimate the optimal configuration of the proposed HRES, subject to satisfying the required annual load in the Chikushi campus. The results revealed a potential of 2% surplus power generation from the proposed HRES, using the FLC-based MPPT system, which can guarantee a lower levelized cost of electricity (LOCE) for the HRES and significant savings of 2.17 million yen per year. The TEA results show that reducing the cost of the solar system market will lead to a reduction in LCOE of the HRES in 2030.

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