| Autor: |
Santos de Araújo JV; Renewable and Alternatives Energies Center (CEAR), Electrical Engineering Department (DEE), Campus I, Federal University of Paraiba (UFPB), João Pessoa 58051-900, Brazil., de Lucena MP; Renewable and Alternatives Energies Center (CEAR), Department of Renewable Energy Engineering (DEER), Campus I, Federal University of Paraiba (UFPB), João Pessoa 58051-900, Brazil., da Silva Netto AV; Renewable and Alternatives Energies Center (CEAR), Electrical Engineering Department (DEE), Campus I, Federal University of Paraiba (UFPB), João Pessoa 58051-900, Brazil., Gomes FDSV; Renewable and Alternatives Energies Center (CEAR), Department of Renewable Energy Engineering (DEER), Campus I, Federal University of Paraiba (UFPB), João Pessoa 58051-900, Brazil., Oliveira KC; Renewable and Alternatives Energies Center (CEAR), Department of Renewable Energy Engineering (DEER), Campus I, Federal University of Paraiba (UFPB), João Pessoa 58051-900, Brazil., de Souza Neto JMR; Renewable and Alternatives Energies Center (CEAR), Electrical Engineering Department (DEE), Campus I, Federal University of Paraiba (UFPB), João Pessoa 58051-900, Brazil., Cavalcante SL; Renewable and Alternatives Energies Center (CEAR), Department of Renewable Energy Engineering (DEER), Campus I, Federal University of Paraiba (UFPB), João Pessoa 58051-900, Brazil., Morales LRV; Huawei Digital Power Brazil, São Paulo 04711-904, Brazil., Villanueva JMM; Renewable and Alternatives Energies Center (CEAR), Electrical Engineering Department (DEE), Campus I, Federal University of Paraiba (UFPB), João Pessoa 58051-900, Brazil., Macedo ECT; Renewable and Alternatives Energies Center (CEAR), Electrical Engineering Department (DEE), Campus I, Federal University of Paraiba (UFPB), João Pessoa 58051-900, Brazil. |
| Abstrakt: |
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. |
