A 24-sectors direct power control-feedforward neural network method of DFIG integrated to dual-rotor wind turbine

<p class="Abstract">In this work, a 24-sector direct power control (24-sector DPC) of a doubly-fed induction generator (DFIG) based dual-rotor wind turbine (DRWT) is studied. The major disadvantage of the 24-DPC control is the steady-state ripples in reactive and active powers. The use of 24 sectors of rotor flux, a feedforward neural network (FNN) algorithm is proposed to improve traditional 24-sector DPC performance and minimize significantly harmonic distortion (THD) of stator current and reactive/active power ripple. The proposed method is modeled and simulated by using MATLAB/Simulink software under different tests and compared with conventional 24-sector DPC.</p>

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A Complete Modeling and Control for Wind Turbine Based of a Doubly Fed Induction Generator using Direct Power Control

International Journal of Power Electronics and Drive Systems (IJPEDS) ◽

10.11591/ijpeds.v8.i4.pp1954-1962 ◽

2017 ◽

Vol 8 (4) ◽

pp. 1954 ◽

Cited By ~ 1

Author(s):

Fawzi Senani

Keyword(s):

Power Control ◽

Wind Turbine ◽

Control Strategy ◽

Reactive Power ◽

Maximum Efficiency ◽

Direct Power ◽

Direct Power Control ◽

Modeling And Control ◽

Doubly Fed ◽

And Control

<span lang="EN-US">The paper presents the complete modeling and control strategy of variable speed wind turbine system (WTS) driven doubly fed induction generators (DFIG). A back-to-back converter is employed for the power conversion exchanged between DFIG and grid. The wind turbine is operated at the maximum power point tracking (MPPT) mode its maximum efficiency. Direct power control (DPC) based on selecting of the appropriate rotor voltage vectors and the errors of the active and reactive power, the control strategy of rotor side converter combines the technique of MPPT and direct power control. In the control system of the grid side converter the direct power control has been used to maintain a constant DC-Link voltage, and the reactive power is set to 0. Simulations results using MATLAB/SIMULINK are presented and discussed on a 1.5MW DFIG wind generation system demonstrate the effectiveness of the proposed control.</span>

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Direct Power Control Optimization for Doubly Fed Induction Generator Based Wind Turbine Systems

Mathematical and Computational Applications ◽

10.3390/mca24030077 ◽

2019 ◽

Vol 24 (3) ◽

pp. 77 ◽

Cited By ~ 5

Author(s):

Alhato ◽

Bouallègue

Keyword(s):

Power Control ◽

Wind Turbine ◽

Optimization Problem ◽

Induction Generator ◽

Doubly Fed Induction Generator ◽

Direct Power ◽

Direct Power Control ◽

Grasshopper Optimization Algorithm ◽

Pi Controllers ◽

Doubly Fed

This study presents an intelligent metaheuristics-based design procedure for the Proportional-Integral (PI) controllers tuning in the direct power control scheme for 1.5 MW Doubly Fed Induction Generator (DFIG) based Wind Turbine (WT) systems. The PI controllers’ gains tuning is formulated as a constrained optimization problem under nonlinear and non-smooth operational constraints. Such a formulated tuning problem is efficiently solved by means of the proposed Thermal Exchange Optimization (TEO) algorithm. To evaluate the effectiveness of the introduced TEO metaheuristic, an empirical comparison study with the homologous particle swarm optimization, genetic algorithm, harmony search algorithm, water cycle algorithm, and grasshopper optimization algorithm is achieved. The proposed TEO algorithm is ensured to perform several desired operational characteristics of DFIG for the active/reactive power and DC-link voltage simultaneously. This is performed by solving a multi‐objective function optimization problem through a weighted‐sum approach. The proposed control strategy is investigated in MATLAB/environment and the results proved the capabilities of the proposed control system in tracking and control under different scenarios. Moreover, a statistical analysis using non-parametric Friedman and Bonferroni–Dunn’s tests demonstrates that the TEO algorithm gives very competitive results in solving global optimization problems in comparison to the other reported metaheuristic algorithms.

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