Browsing by Author "Astudillo Astudillo, Walter Ramiro"

Now showing 1 - 2 of 2

Estudio y desarrollo de un prototipo de estación de carga nivel L2 para vehículos eléctricos
(2018-05-07) Astudillo Astudillo, Walter Ramiro; Caguana Buele, Dario Xavier; Gonzalez Morales, Luis Gerardo
This work presents the study and development of a prototype of a charging station level L2. These stations are the most common type currently used in homes, garages, buildings and public places. These stations must be easily operated by users and must offer adequate security measures. For the development of a charging station it is necessary to know the norms and standards that are used by the different electric vehicle manufacturers, it is also necessary to know about programming of microcontrollers, digital and analog electronics since the charging station works with high levels of power. The prototype developed has dimensions of 300x250x120 mm, thanks to this small size can be implemented in domestic stations for personal use, as well as public stations, located throughout a city. The charging station is able to theoretically handle a maximum power level of 6.6 KW, however, after several tests at rated power, the electrical system delivered a maximum of 5.3 KW. The charging station has some additional features such as a display, a keyboard for its operation, an internet connection that allows the user to operate the station remotely, as well as receiving some parameters from the station on a mobile device.
Evaluación de un algoritmo basado en Machine Learning para un flujo de potencia óptima de corriente alterna ACOPF
(Universidad de Cuenca, 2022-10-21) Astudillo Astudillo, Walter Ramiro; Astudillo Salinas, Darwin Fabián
In this work, the feasibility of using machine learning (ML) to obtain solutions to the alternating current optimal power flow problem ACOPF (Alternating Current Optimal Power Flow) is analyzed. Because ACOPF is a nonconvex problem with high nonlinearity, numerous efforts have been made to find efficient optimization methods that can substantially reduce resolution times. OPF (Optimal Power Flow) problems are usually solved by interior point methods [1], also known as barrier methods. One of the most widely used approaches is the primary dual interior point technique with a filter line search [2]. These methods are robust but expensive, since they require the calculation of the second derivative of the Lagrangian in each iteration. A new and fruitful research direction is to use ML techniques to solve problems of operation and control of electrical networks. ML has been shown to significantly reduce the use of computational resources in many real-world problems. Several solution methods have been used, among them random forest, multi-objective decision tree and extreme learning machine [3, 4]. The ML operation in this case is applied as a method that first predicts voltage magnitudes and angles on each bus. Using network equations based on physics to calculate the injection of power in different buses. For general ML learning, the data is divided into three sets: one for training, one for validation, and finally, one for testing. These algorithms focus on minimizing their objective function and the cost of operating an AC transmission network.