Wind turbine yaw angle controller using artificial neural networks implemented on embedded system

En esta investigación, las redes neuronales artificiales (RNA) desarrolladas en python se comparan y luego se compilan en una Raspberry pi 4 para generar una señal predictiva de la dirección del viento como aerogenerador Sistema de control de entrada, para maximizar la captación de energía eólica. Se utiliza un conjunto de 12 variables medidas de la estación meteorológica para alimentar la red neuronal, incluido el tiempo, PM10, PM25 y ozono como variables secundarias que permitirán enriquecer la capacidad predictiva factores de la red neuronal, las variables NO, NO2, NOX, y SO2, como variables auxiliares que permitirán fortalecer la validación del comportamiento de la red y finalmente la variables Velocidad del viento, temperatura, humedad relativa y viento dirección como principales variables que aumentarán la predicción eficiencia y con ello, completar la dependencia parcial entre las variables se analiza para mejorar la RNA tiempo de convergencia en el sistema embebido, como trabajo futuro, se permitirá la prueba de un sistema de control incluyendo el control actuadores para optimizar la redIn this research, artificial neural networks (ANN) developed in python are compared and later compiled in a Raspberry pi 4 to generate a predictive wind direction signal as wind turbine control system input, to maximize the capture of wind power. A set of 12 weather station measured variables are used to feed the neural network, including time, PM10, PM25, and Ozone as secondary variables that will allow enriching the predictive factors of the neural network, the variables NO, NO2, NOX, and SO2, as auxiliary variables that will allow strengthening the validation of the behavior of the network and finally the variables Wind Speed, temperature, relative humidity and wind direction as main variables that will increase the prediction efficiency and with this, to complete partial dependence between the variables is analyzed to improve the ANN convergence time on the embedded system, as future work, it will allow the testing of a control system including control actuators to optimize the networ

Approaches to stochastic modeling of wind turbines

Background. This paper study statistical data gathered from wind turbines located on the territory of the Republic of Poland. The research is aimed to construct the stochastic model that predicts the change of wind speed with time. Purpose. The purpose of this work is to find the optimal distribution for the approximation of available statistical data on wind speed. Methods. We consider four distributions of a random variable: Log-Normal, Weibull, Gamma and Beta. In order to evaluate the parameters of distributions we use method of maximum likelihood. To assess the the results of approximation we use a quantile-quantile plot. Results. All the considered distributions properly approximate the available data. The Weibull distribution shows the best results for the extreme values of the wind speed. Conclusions. The results of the analysis are consistent. © ECMS Zita Zoltay Paprika, Péter Horák, Kata Váradi,Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, János Péter Rádics (Editors)

Approaches to stochastic modeling of wind turbines

Background. This paper study statistical data gathered from wind turbines located on the territory of the Republic of Poland. The research is aimed to construct the stochastic model that predicts the change of wind speed with time. Purpose. The purpose of this work is to find the optimal distribution for the approximation of available statistical data on wind speed. Methods. We consider four distributions of a random variable: Log-Normal, Weibull, Gamma and Beta. In order to evaluate the parameters of distributions we use method of maximum likelihood. To assess the the results of approximation we use a quantile-quantile plot. Results. All the considered distributions properly approximate the available data. The Weibull distribution shows the best results for the extreme values of the wind speed. Conclusions. The results of the analysis are consistent. © ECMS Zita Zoltay Paprika, Péter Horák, Kata Váradi,Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, János Péter Rádics (Editors)