A Novel Model-Based Predictive Direct Power Control of Doubly-Fed Induction Generator

2012 ◽  
pp. 493-498 ◽  
Author(s):  
Alireza Nazari ◽  
Hossein Heydari
Keyword(s):  
Power Control ◽  
Induction Generator ◽  
Doubly Fed Induction Generator ◽  
Direct Power ◽  
Direct Power Control ◽  
Model Based ◽  
Doubly Fed ◽  
Novel Model

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

2019 ◽  
Vol 24 (3) ◽  
pp. 77 ◽  
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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