Enhancing the control of doubly fed induction generators using artificial neural networks in the presence of real wind profiles

This study tackles the complex task of integrating wind energy systems into the electric grid, facing challenges such as power oscillations and unreliable energy generation due to fluctuating wind speeds. Focused on wind energy conversion systems, particularly those utilizing double-fed induction generators (DFIGs), the research introduces a novel approach to enhance Direct Power Control (DPC) effectiveness. Traditional DPC, while simple, encounters issues like torque ripples and reduced power quality due to a hysteresis controller. In response, the study proposes an innovative DPC method for DFIGs using artificial neural networks (ANNs). Experimental verification shows ANNs effectively addressing issues with the hysteresis controller and switching table. Additionally, the study addresses wind speed variability by employing an artificial neural network to directly control reactive and active power of DFIG, aiming to minimize challenges with varying wind speeds. Results highlight the effectiveness and reliability of the developed intelligent strategy, outperforming traditional methods by reducing current harmonics and improving dynamic response. This research contributes valuable insights into enhancing the performance and reliability of renewable energy systems, advancing solutions for wind energy integration complexities

Optimal design of an horizontal axis wind turbine using blade element momentum theory

The aerodynamic modelling of the wind turbine blades is a vital step in the design of the turbine. Several design methods are available for the aerodynamic design of the rotor, however, in this study a mathematical model based on blade element momentum concept is applied. the purpose of this work is to optimize the distribution of chord and twist angle along the blade span of a 20 KW HAWT using the BEM method, the blade design parameters such as the optimum lift and drag coefficient , chord and twist angle, the axial induction factor(a) and the angular induction factor(a’) of the designed HAWT are codified and estimated using the MATLAB software. The results of the analysis show on the one hand a decrease in chord length and twist angle along the blade length and on the other hand the maximum values that can be achieved by the axial and angular inductions factors are respectively 0.3325 and 0.175. This approach can be effectively implemented for the analysis of HAWTs operating at different characteristics of the designed blade

New method for wind potential prediction using recurrent artificial neural networks

The aim of the study is to find the right architecture of the NARX neural network, in order to perform the daily prediction of the maximum wind speed of Laayoune city. We relied on the Levenberg-Marquardt optimization algorithm. The RMSE error metric showed that NARX-SP outperforms NARX-P

Designing wind turbine airfoil shapes through genetic algorithm: A first approach

[Resumen] El objetivo principal en el diseño de las palas de aerogeneradores es el uso de perfiles aerodinámicos adecuados para aumentar el rendimiento aerodinámico y disminuir el coste de la energía. El objetivo de esta investigación es emplear técnicas de optimización numérica para desarrollar perfiles aerodinámicos de aerogeneradores. Para alcanzar este objetivo, se formula un modelo matemático combinando algoritmos genéticos con el solucionador de flujo XFOIL. Se crean dos perfiles aerodinámicos distintos con características diferentes utilizando el algoritmo genético. A lo largo del procedimiento de optimización, se utiliza el software XFOIL para determinar los coeficientes de sustentación y resistencia. Este estudio confirma la viabilidad y eficacia del innovador enfoque de diseño. Además, proporciona un valioso concepto de diseño que puede aplicarse eficazmente a los perfiles aerodinámicos de aerogeneradores de espesor medio.[Abstract] The primary goal in the aerodynamic design of wind turbines is the use of appropriate airfoils in order to increase the aerodynamic performance and to decrease the cost of energy. The goal of this research is to employ numerical optimization techniques to develop wind turbine airfoils. To attain this objective, a mathematical model is formulated by combining the genetic algorithm with the XFOIL flow solver. Two distinct airfoils with different characteristics are created using the genetic algorithm. Throughout the optimization procedure, XFOIL software is utilized to determine the lift and drag coefficients. This study confirms the viability and efficiency of the innovative design approach. Furthermore, it provides a valuable design concept that can effectively applied to wind turbine airfoils with medium thickness.Ministerio de Ciencia e Innovación; MCI/AEI/FEDER PDI2021-123543OB-C2