For decades, maximum power coefficient limit, known as the Betz limit, has been accepted as a theoretical optimum value for wind turbine power extraction; nevertheless, some reports, exceeding this limit, have already been published. To explain this phenomenon and show a different point of view, a novel theoretical and ideal analysis based on flow conservation law and areas’ quotient is presented, supported by a review of works related to surpassing the power coefficient limit approached from different perspectives.

Maximum power point tracking in wind turbines is a topic that has attracted many researchers' interest; however, the studies presented are usually carried out only at the simulation level, so they lack a verification in the system through real measurements. On the other hand, the system's modeling is usually quite complex, making it challenging to meet the control objectives. There are unified models in which the system is treated in a generalized way according to various research purposes. This work presents a methodology that simplifies the unified system through a series of dynamic tests that applied to obtained a simplified model much easier to handle without sacrificing the system's dynamic richness.

Mathematical models and algorithms for maximizing power extraction have become an essential topic in renewable energies in the last years, especially in wind energy conversion systems. This study proposes maximum power point tracking using gain scheduling approximations for an emulated wind system in a direct-drive connection. Power extraction is obtained by controlling the duty cycle of a Multilevel Boost Converter, which directly varies the rotational speed of a permanent magnet synchronous generator directly coupled to a three-phase induction motor that emulates the wind turbine. The system’s complexity is linked to the inherent non-linearities associated with the diverse electrical, mechanical, and power electronic elements. In order to present a synthesized model without losing the system dynamic richness, several physical tests were made to obtain parameters for building several mathematical approaches, resulting in non-linear dynamic equations for the controller gains, which are dependant on wind speed. Thirty real operational wind speeds considering typical variations were used in several tests to demonstrate the mathematical models’ performance. Results among these gain scheduling approaches and a typical controller constant gains mathematical model were compared based on standard deviations, absolute error, and the time for reaching the optimum generator angular speed related to every wind speed.

The database here contains experimental data relevant to an original maximum power point tracking controller for an experimental direct-drive full-variable-speed full-rated converter Type IV Wind Energy Conversion System in standalone operation. The main goal is to maximize power extraction by controlling the duty cycle of a multilevel boost converter, which is responsible for adjusting the angular speed of a permanent magnet synchronous generator coupled to a three-phase induction motor that emulates the wind turbine. Two data acquisition cards with the appropriate signal conditioners were used to obtain measurements of the generator angular speed, output current, and output voltage at the terminals of the multilevel converter. In addition, data related to power coefficient, tip speed ratio, duty cycle, and output power are also included. Two PCs in a Linux real-time platform were used for the emulation, control, and data collection processes. On the other hand, Matlab was used to analyze the data to evaluate the controller's performance to maximize wind power extraction. The database is freely accessible at http://dx.doi.org/10.17632/363d24mcb6.2. This dataset [1] represents a resource for wind power specialists who develop algorithms for wind energy optimization.

This research aims to integrate a Conversational System in virtual educational environments, reflecting the three dimensions of educational knowledge: know – know, know – do and know – be. Its purpose is to improve the personalization and quality of online learning through educational coaching. Although online interaction is increasingly relevant, current systems do not holistically integrate these three areas of knowledge. This study focuses on modeling "knowing – being", using speech act theory, Socratic dialectic and personality profile to generate responses that encourage active and reflective listening. Through natural language processing, the intelligent agent can interact more effectively, acting as a virtual coach that provides empathetic and personalized feedback. The research seeks to optimize virtual learning environments and advance the simulation of realistic interactions with virtual entities.