Abstract
A hybrid neural network approach based tool for identifying the photovoltaic one-diode model is presented. The generalization capabilities of neural networks are used together with the robustness of the reduced form of one-diode model. Indeed, from the studies performed by the authors and the works present in the literature, it was found that a direct computation of the five parameters via multiple inputs and multiple outputs neural network is a very difficult task. The reduced form consists in a series of explicit formulae for the support to the neural network that, in our case, is aimed at predicting just two parameters among the five ones identifying the model: the other three parameters are computed by reduced form. The present hybrid approach is efficient from the computational cost point of view and accurate in the estimation of the five parameters. It constitutes a complete and extremely easy tool suitable to be implemented in a microcontroller based architecture. Validations are made on about 10000 PV panels belonging to the California Energy Commission database.
Highlights
Nowadays, the photovoltaic (PV) based generation systems are extremely common and you can view both small plants on the roof top or larger plants (MWpp) usually in rural or industrial environments
From the studies performed by the authors and the works present in the literature, it was found that a direct computation of the five parameters via multiple inputs and multiple outputs neural network is a very difficult task
By following our previous successful experiences in the application of neural networks (NNs) to the PV field, we propose a solution of the identification problem for the five-parameter model starting from few information, thanks to the synergy between a neural network and an analytical approach, the so-called reduced forms of the one-diode model
Summary
The photovoltaic (PV) based generation systems are extremely common and you can view both small plants (some kWpp) on the roof top or larger plants (MWpp) usually in rural or industrial environments. There is a scarcity of embedded systems able to characterize in real time the PV arrays during their normal working in order to update the parameters of the PV model for a better estimation of generated power The reason of this lack is essentially due to two issues: (i) the requirement of several sensors for the continuous monitoring of the PV plants and (ii) the difficulty of identifying in real time the PV model, since this requires the solution of a transcendental (nonlinear) problem, the five-parameter model, which is really hard to solve without the use of suitable computing environment such as Matlab, Mathematica, and Maple.
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