Analysis of the Technical Feasibility of Using Artificial Intelligence for Smoothing Active Power in a Photovoltaic System Connected to the Power System

• Published in: Brazilian Archives of Biology and Technology Vol. 64; no. spe
• Main Authors: Torres, Norah Nadia Sánchez, Diaz, Valentin Nicolas Silvera, Ando Junior, Oswaldo Hideo, Ledesma, Jorge Javier Gimenez
• Format: Journal Article
• Language: English
• Published: Instituto de Tecnologia do Paraná (Tecpar) 01-01-2021
• Subjects: Photovoltaic system connected to the grid; Power smoothing and artificial neural network
• DOI: 10.1590/1678-4324-75years-2021210196
• EISSN: 1678-4324
• ISSN: 1516-8913

Abstract Recent technological advances and increased participation of energy systems based on photovoltaic solar energy place this renewable energy source in a prominent position in the current scenario. With the increase in the share of solar photovoltaic systems, the impact of power fluctuations in these sources has worsened, which can affect the quality of electrical energy and the reliability of the electrical power system. Therefore, with the use of energy storage together with control algorithms based on artificial intelligence, it is possible to control and perform power smoothing. In this context, the study presents a technical feasibility study on the use of artificial neural network (ANN) to perform the power smoothing of the photovoltaic system connected to the network. Being studied the performance of a real photovoltaic system operating in conjunction with an ideal energy storage for comparative analysis of the performance of the artificial neural network when the numbers of neurons and layers are modified for different real operating conditions considered as temperature variation, humidity, irradiation, pressure and wind speed, which are considered to be ANN input data. The results obtained point to the feasibility of using ANN, with acceptable precision, for power smoothing. According to the analyzes carried out, it is clear that ANN's with few neurons, the smoothing profile tends to be more accurate when compared to larger amounts of neurons. In the current state of the study, it was not possible to determine a relationship between the variations in the number of neurons with the most accurate results, it is important to note that the development of the curve pointed by the neural network can be influenced by the database. It should be noted that, when ANN exceeds or does not reach the optimal smoothing curve, the storage system compensates for the lack or excess of power, and there is a need for other mechanisms to optimize power smoothing.