Neuro-Fuzzy-Based Solar Cell Model

Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review

“…Based on a set of rules defined in the fuzzy associative memory, each parameter is adjusted to fit the calculated I-V curve to the experimental. Abdulhadi et al [111] developed a hybrid neuro-fuzzy ANN (NF-ANN) technique for PV arrays. Compared to pure ANN, the NF-ANN requires less data for training, thus more suitable for newly installed PV systems, where measured data is limited.…”

Artificial intelligence techniques for photovoltaic applications: A review

“…The second application is proposed by Abdulhadi et al [318] in which the authors described a hybrid softcomputing modeling technique that facilitates the modeling of newly installed solar cells, with few historical measured data, over a range of expected operating conditions.…”

Photovoltaic prognostics and heath management using learning algorithms

“…The models in conjunction with current, voltage, or power measurements from the physical system are used to detect a number of fault conditions such as shading [2,[5][6][7][8] , inverter failure [5][6]8], snow cover [5][6], module failures or short circuiting [4,[7][8], and string-level malfunctions [2,[5][6]. Learning algorithms [4,[8][9], Bayesian networks [10], and fuzzy logic [11][12] have also been used successfully to estimate PV output or perform fault diagnoses. Unfortunately, most of these systems are designed to detect catastrophic failures and do not monitor system degradation over time.…”