Neural Modeling of Fuzzy Controllers for Maximum Power Point Tracking in Photovoltaic Energy Systems


Manuel Lopez-Guede J., Ramos-Hernanz J., ALTIN N., ÖZDEMİR Ş., KURT E., Azkune G.

JOURNAL OF ELECTRONIC MATERIALS, cilt.47, sa.8, ss.4519-4532, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 47 Sayı: 8
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1007/s11664-018-6407-2
  • Dergi Adı: JOURNAL OF ELECTRONIC MATERIALS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.4519-4532
  • Anahtar Kelimeler: Fuzzy logic control, FLC, artificial neural networks, ANN, photovoltaic systems, BOOST CONVERTER, CELL
  • Gazi Üniversitesi Adresli: Evet

Özet

One field in which electronic materials have an important role is energy generation, especially within the scope of photovoltaic energy. This paper deals with one of the most relevant enabling technologies within that scope, i.e, the algorithms for maximum power point tracking implemented in the direct current to direct current converters and its modeling through artificial neural networks (ANNs). More specifically, as a proof of concept, we have addressed the problem of modeling a fuzzy logic controller that has shown its performance in previous works, and more specifically the dimensionless duty cycle signal that controls a quadratic boost converter. We achieved a very accurate model since the obtained medium squared error is 3.47 x 10(-6), the maximum error is 16.32 x 10(-3) and the regression coefficient R is 0.99992, all for the test dataset. This neural implementation has obvious advantages such as a higher fault tolerance and a simpler implementation, dispensing with all the complex elements needed to run a fuzzy controller (fuzzifier, defuzzifier, inference engine and knowledge base) because, ultimately, ANNs are sums and products.