El seguimiento del punto de máxima potencia (MPPT) es una etapa esencial en la operación de las turbinas eólicas para garantizar una generación de energía eficiente. En los últimos años se han diseñado y aplicado técnicas de control avanzadas para lograr este objetivo, solventando algunas de las limitaciones de los métodos clásicos. Este artículo proporciona una visión general de las estrategias existentes y describe con más detalle algunas configuraciones de control específicas, explicando su utilidad y proporcionando una base para futuros desarrollos. En concreto incluye técnicas de control basadas en inteligencia artificial para el estudio del control MPPT en aerogeneradores. Se ejemplifican dos estrategias de control inteligente: una red neuronal y un controlador de lógica borrosa. Estos enfoques se enmarcan en la regulación del par electromagnético del generador y, en consecuencia, de la velocidad angular del sistema, mejorando la generación de potencia. Los resultados evidencian los beneficios de estos controladores inteligentes para maximizar la potencia y mejorar el proceso de conversión de energía.

The main control goal of a wind turbine (WT) is to produce the maximum energy in any operating region. When the wind speed is under its rated value, the control must aim at tracking the maximum power point of the best power curve for a specific WT. This is challenging due to the non-linear characteristics of the system and the environmental disturbances it is subjected to. Direct speed control (DSC) is one of the main techniques applied to address this problem. In this strategy, it is necessary to design a speed controller to adjust the generator torque so to follow the optimum generator speed. In this work, we improve the DSC by implementing this speed controller with a radial basis function neural network (NN). An unsupervised learning algorithm is designed to tune the weights of the NN so it learns the control law that minimizes the generator speed error. With this proposed unsupervised neural control methodology, the electromagnetic torque that allows the optimal power extraction is obtained, and thus the best power coefficient (${C}_\mathrm{p}$) values. The proposal is tested on the OpenFAST non-linear model of the National Renewable Energy Laboratory 1.5 MW WT. Simulation results prove the good performance of this neuro-control approach as it maintains the WT variables into the appropriate range and tracks the rated operation values. It has been compared with the controller included in OpenFAST giving up to 7.87% more power.

In this In this article, a control strategy for Maximum Power Point Tracking (MPPT) of a wind turbine system based on a Doubly Fed Induction Generator (DFIG) is presented. The proposed strategy consists of the Indirect Speed Control (ISC) taking the Low Speed Shaft (LSS) as variable input. This implementation allows the MPPT to optimize the Power coefficient (Cp). The controller has been designed in order to allow the wind turbine to reach the MPPT along the partial load operation. For these experiments, a 1.5 MW wind turbine was modeled and simulated by using Matlab and Fatigue, Aerodynamic, Structure and Turbulence (FAST) software. In order to present the achieved results, a comparison between the ISC and a classical PI controller is made. The Cp curves as well as the output power display an important improvement in terms of stability. These results are possible because the appropriate values of optimal Tip Speed Ratio (TSR) and maximum Cp have been properly established. article, a control strategy for Maximum Power Point Tracking (MPPT) of a wind turbine system based on a Doubly Fed Induction Generator (DFIG) is presented. The proposed strategy consists of the Indirect Speed Control (ISC) taking the Low Speed Shaft (LSS) as variable input. This implementation allows the MPPT to optimize the Power coefficient (Cp). The controller has been designed in order to allow the wind turbine to reach the MPPT along the partial load operation. For these experiments, a 1.5 MW wind turbine was modeled and simulated by using Matlab and Fatigue, Aerodynamic, Structure and Turbulence (FAST) software. In order to present the achieved results, a comparison between the ISC and a classical PI controller is made. The Cp curves as well as the output power display an important improvement in terms of stability. These results are possible because the appropriate values of optimal Tip Speed Ratio (TSR) and maximum Cp have been properly established.

Wind turbines are complex energy converters that require robust control solutions to address performance limitations caused by their mechatronic systems and external disturbances. Operating below the nominal power region implies significant challenges in achieving a precise, efficient response while maintaining structural stability and reducing vibrations. This study proposes a control architecture aimed at maximizing power generation and minimizing structural vibrations. Four hybrid control strategies are developed based on radial basis function neural networks (RBFNNs) combined with conventional regulators to address this dual objective. The proposed controllers compute the appropriate electromagnetic torque to track the maximum power point while mitigating tower acceleration. The RBFNNs utilize a non-supervised learning algorithm to adaptively adjust their weights, enabling better coupling with wind turbine dynamics. The hybrid control strategies were tested on a 5 MW floating offshore wind turbine subjected to the combined effects of wind and wave disturbances. Simulation results demonstrate that these methods achieve a more efficient power response while significantly reducing structural fatigue. The proposed hybrid strategies effectively enhance wind turbine performance by improving power generation efficiency and reducing mechanical stress, thereby extending the turbine's operational lifetime.