MAXIMUM POWER POINT PREDICTION AND WIND POWER SYSTEM TRACKING

In this work, a new intelligent method is developed to predict the maximum power point and to wind system tracking and increase their productivity, reliability and quality of energy, which reduces the cost of wind Kwh. The method developed combines two intelligent techniques (prediction and tracking), which at first allows prediction of a maximum power point, which is then corrected by the application of fuzzy logic, this results in a rapid and accurate convergence to the optimum power point. The generator used in this study is a synchronous permanent magnet (PMSG), controlled by an electronic converter with Pulse Width Modulation (PWM); this last use of a vector and an MPPT (Maximum Power Point Tracking) controller to check the electromechanical magnitudes such as the torque or the rotational speed of the generator in order to extract the maximum wind energy. The simulation results show the effectiveness and robustness of the proposed control

Introduction to the Special Section on Artificial Intelligence in Renewable Energetic Systems

The world’s today is in an unprecedented and urgent need to optimize energy consumption and accelerate the transition towards green, low-carbon electricity generation. To face the various challenges in the digital transformation of the energy ecosystem, technologies such as Big Data Analytics, Machine Learning and Artificial Intelligence (AI) are poised to play an increasingly important role in the years ahead. Expanding the adoption of AI technology across the energy sector will enable better control and management of energy consumption, anticipating network malfunctions, or even optimizing infrastructure assets. The aim of this special section is to disseminate the latest and ongoing research and technological advancements on the application of AI in modeling, control and optimization of Renewable and Alternative Energy Systems and future smart electricity networks.Peer reviewe