Estimation of the maximum power and normal operating power of a photovoltaic module by neural networks

A.B.g Bahgat, N.h Helwa, G.e Ahamd and E.t El Shenawy

Renewable Energy, 2004, vol. 29, issue 3, 443-457

Abstract: This paper presents an application of the neural networks for identification of the maximum power (MP) and the normal operating power (NOP) of a photovoltaic (PV) module. Two neural networks are developed; the first is the maximum power neural network (MPNN) and the second is the normal operating power neural network (NOPNN). The two neural networks receive the solar radiation and the PV module surface temperature as inputs, and estimate the MP and the NOP of a PV module as outputs. The training process for the two neural networks used a series of input/output data pairs. The training inputs are the solar radiation and the PV module surface temperature, while the outputs are the PV module MP for the MPNN and the PV module NOP for the NOPNN. The results showed that, the proposed neural networks introduced a good accurate prediction for the PV module MP and NOP compared with the measured values.

Keywords: PV module; Maximum power point; Neural networks; Training process (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:29:y:2004:i:3:p:443-457

DOI: 10.1016/S0960-1481(03)00126-5

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