Novel Deep Learning Model for Predicting Wind Velocity and Power Estimation in Advanced INVELOX Wind Turbines

Wind energy is a renewable energy source that has grown rapidly in recent decades. This energy is converted into electricity using advanced INVELOX wind turbines. However, the wind velocity is critical, and predicting this velocity in real-time is challenging. As a result, a deep learning (DL) model has been developed to predict the velocity in advanced wind turbines using a novel enhanced Long Short-Term Memory (LSTM) model. The LSTM enhancement is executed by employing the Black Widow optimization with Mayfly optimization in the Python platform as application software. The dataset has been prepared using Ansys Fluent fluid flow analysis. In addition to that, the wind turbine power generation was computed analytically. A subsonic wind tunnel test is also performed by employing a 3-Dimensional printed physical model to validate the simulation dataset for this innovative design. The proposed MFBW-LSTM model (Enhanced LSTM with BWO and MFO) predicts efficiently, with an accuracy of 95.34%. Furthermore, the performance of the proposed model is compared to LSTM, BW-LSTM, and MF-LSTM. Accuracy, MAE, MAPE, MSE, and RMSE are among the performance criteria the proposed DL model achieves efficiently. As a result, the proposed DL model is best suited for velocity prediction of an Advanced INVELOX wind turbine in various cross sections with high accuracy

Neural networks and reinforcement learning in wind turbine control

[EN] Pitch control of wind turbines is complex due to the intrinsic non-linear behavior of these devices, and the external disturbances they are subjected to related to changing wind conditions and other meteorological phenomena. This difficulty is even higher in the case of floating offshore turbines, due to ocean currents and waves. Neural networks and other intelligent control techniques have been proven very useful for the modeling and control of these complex systems. Thus, this paper presents different intelligent control configurations applied to wind turbine pitch control. Direct pitch control based on neural networks and reinforcement learning, and some hybrid control configurations are described. The usefulness of neuro-estimators for the improvement of controllers is also presented. Finally, some of these techniques are used in an application example with a land wind turbine model.[ES] El control del ángulo de las palas de las turbinas eólicas es complejo debido al comportamiento no lineal de los aerogeneradores, y a las perturbaciones externas a las que están sometidas debido a las condiciones cambiantes del viento y otros fenómenos meteorológicos. Esta dificultad se agrava en el caso de las turbinas flotantes marinas, donde también les afectan las corrientes marinas y las olas. Las redes neuronales, y otras técnicas del control inteligente, han demostrado ser muy útiles para el modelado y control de estos sistemas. En este trabajo se presentan diferentes configuraciones de control inteligente, basadas principalmente en redes neuronales y aprendizaje por refuerzo, aplicadas al control de las turbinas eólicas. Se describe el control directo del ángulo de las palas del aerogenerador y algunas configuraciones híbridas de control. 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Machine Learning and Data Mining Applications in Power Systems

This Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods to facilitate the development of modern electric power systems, grids and devices, and smart grids and protection devices, as well as to develop tools for more accurate and efficient power system analysis. Conventional signal processing is no longer adequate to extract all the relevant information from distorted signals through filtering, estimation, and detection to facilitate decision-making and control actions. Machine learning algorithms, optimization techniques and efficient numerical algorithms, distributed signal processing, machine learning, data-mining statistical signal detection, and estimation may help to solve contemporary challenges in modern power systems. The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency; the dynamic optimization of grid operations; demand response; the incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and the integration of smart appliances and consumer devices. Signal processing offers the tools needed to convert measurement data to information, and to transform information into actionable intelligence. This Special Issue includes fifteen articles, authored by international research teams from several countries