Solar Tracking Control Algorithm Based on Artificial Intelligence Applied to Large-Scale Bifacial Photovoltaic Power Plants
José Vinícius Santos de Araújo,
Micael Praxedes de Lucena,
Ademar Virgolino da Silva Netto,
Flávio da Silva Vitorino Gomes,
Kleber Carneiro de Oliveira,
José Mauricio Ramos de Souza Neto,
Sidneia Lira Cavalcante,
Luis Roberto Valer Morales,
Juan Moises Mauricio Villanueva,
Euler Cássio Tavares de Macedo
Affiliations
José Vinícius Santos de Araújo
Renewable and Alternatives Energies Center (CEAR), Electrical Engineering Department (DEE), Campus I, Federal University of Paraiba (UFPB), João Pessoa 58051-900, Brazil
Micael Praxedes de Lucena
Renewable and Alternatives Energies Center (CEAR), Department of Renewable Energy Engineering (DEER), Campus I, Federal University of Paraiba (UFPB), João Pessoa 58051-900, Brazil
Ademar Virgolino da Silva Netto
Renewable and Alternatives Energies Center (CEAR), Electrical Engineering Department (DEE), Campus I, Federal University of Paraiba (UFPB), João Pessoa 58051-900, Brazil
Flávio da Silva Vitorino Gomes
Renewable and Alternatives Energies Center (CEAR), Department of Renewable Energy Engineering (DEER), Campus I, Federal University of Paraiba (UFPB), João Pessoa 58051-900, Brazil
Kleber Carneiro de Oliveira
Renewable and Alternatives Energies Center (CEAR), Department of Renewable Energy Engineering (DEER), Campus I, Federal University of Paraiba (UFPB), João Pessoa 58051-900, Brazil
José Mauricio Ramos de Souza Neto
Renewable and Alternatives Energies Center (CEAR), Electrical Engineering Department (DEE), Campus I, Federal University of Paraiba (UFPB), João Pessoa 58051-900, Brazil
Sidneia Lira Cavalcante
Renewable and Alternatives Energies Center (CEAR), Department of Renewable Energy Engineering (DEER), Campus I, Federal University of Paraiba (UFPB), João Pessoa 58051-900, Brazil
Luis Roberto Valer Morales
Huawei Digital Power Brazil, São Paulo 04711-904, Brazil
Juan Moises Mauricio Villanueva
Renewable and Alternatives Energies Center (CEAR), Electrical Engineering Department (DEE), Campus I, Federal University of Paraiba (UFPB), João Pessoa 58051-900, Brazil
Euler Cássio Tavares de Macedo
Renewable and Alternatives Energies Center (CEAR), Electrical Engineering Department (DEE), Campus I, Federal University of Paraiba (UFPB), João Pessoa 58051-900, Brazil
The transition to a low-carbon economy is one of the main challenges of our time. In this context, solar energy, along with many other technologies, has been developed to optimize performance. For example, solar trackers follow the sun’s path to increase the generation capacity of photovoltaic plants. However, several factors need consideration to further optimize this process. Important variables include the distance between panels, surface reflectivity, bifacial panels, and climate variations throughout the day. Thus, this paper proposes an artificial intelligence-based algorithm for solar trackers that takes all these factors into account—mainly weather variations and the distance between solar panels. The methodology can be replicated anywhere in the world, and its effectiveness has been validated in a real solar plant with bifacial panels located in northeastern Brazil. The algorithm achieved gains of up to 7.83% on a cloudy day and obtained an average energy gain of approximately 1.2% when compared to a commercial solar tracker algorithm.
