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The modelling of photovoltaic (PV) solar cells using a hybrid adaptive neuro-fuzzy inference system (ANFIS) algorithm is presented. It is based on the decomposition of the cell output current into photocurrent and junction current. The photocurrent is linearly dependent on solar irradiance and cell temperature; consequently, its analytical computation is done easily. However, the junction current is highly non-linear and depends on cell voltage and temperature. Therefore, its analytical computation is complicated and the manufacturers do not supply any information about this parameter. Moreover, there is no way to measure it physically. Therefore, it is proposed to use the ANFIS algorithm as a powerful technique in order to estimate this current and reconstruct the output PV cell current using the photocurrent. The model validation is based on the gradient descent and chain rule applied to a set of data different than the one used for training process. The advantage of the proposed model is that only one climatic parameter is used as the input to the ANFIS algorithm, which makes it less sensitive to climatic variations.

In this paper, we present a maximum power point technique for photovoltaic (PV) systems based on the array optimal conductance. We have shown that this conductance depends on the cell junction current which is difficult to estimate due its high non-linearity, therefore, an analytical expression is derived to simplify its use. The results show that the proposed technique gives good results with a minimum output power error and responds perfectly to abrupt change in solar radiation.Index Terms-Maximum power point tracking (mppt), optimal conductance, junction current.

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