A novel MPPT algorithm based on optimized artificial neural network by using FPSOGSA for standalone photovoltaic energy systems

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Duman S., Yörükeren N., Altaş İ. H.

NEURAL COMPUTING & APPLICATIONS, cilt.29, ss.257-278, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 29
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1007/s00521-016-2447-9
  • Dergi Adı: NEURAL COMPUTING & APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.257-278
  • Anahtar Kelimeler: MPPT, Photovoltaic energy systems, Artificial neural network, FPSOGSA, Optimization, POWER-POINT-TRACKING, PARTICLE SWARM OPTIMIZATION, FUZZY-LOGIC, PV SYSTEM, OPERATING POINT, CONTROLLER, SEARCH, PERFORMANCE, EFFICIENCY, CONVERTER
  • Kocaeli Üniversitesi Adresli: Evet

Özet

Maximum power point tracking (MPPT) algorithms are used to maximize the output power of the photovoltaic (PV) panel under different temperature and irradiance conditions in photovoltaic energy sources (PVES). In this paper, a novel MPPT method based on optimized artificial neural network by using hybrid particle swarm optimization and gravitational search algorithm based on fuzzy logic (FPSOGSA) is proposed to track the operation of the PV panel in maximum power point (MPP). The performance of the proposed MPPT approach is tested by doing the simulation and experimental studies under different environmental conditions. The proposed method is compared with the conventional perturb and observation (P&O) method for standalone PVES. The results of the comparison the obtained from the simulation and experimental studies demonstrate that the proposed MPPT method provides the reduction oscillations around the MPP and the increased maximum power yield of the PV system in the steady state.