An indirect method for maximum power point tracking for photovoltaic arrays

Abstract

Photovoltaic (PV)arrays have inconsistent output voltages due to a dependence on insolation, wind, and cell temperature. This causes the output power to vary from the maximum power the PV arrays are capable of providing to a load. An indirect method for tracking the maximum power point (MPP) of a PV array is presented. In order to maximize the output power of the PV array, a PIC microcontroller will be used to track the MPP of the solar array using characteristic equations from a curve fitting method that predicts the power versus voltage (P-V) curve for varying cell temperatures. This method approximates the P-V characteristic curve with a fourth-order polynomial. The coefficients of the approximated P-V characteristic curve were determined to be strongly dependent on cell temperature. The strong dependence of the P-V curve on cell temperature allows the P-V characteristic curve of a given system to be written as a function of both panel voltage and cell temperature. The proposed curve fitting method was verified using experimental data collected in the laboratory. Simulation results of the proposed method are also presented.

Identification of Photovoltaic Panel MPPT Using Neuro-Fuzzy Model

“…The most famous conventional MPPT methods are the Perturb Observe (Hill climbing) method [22,23], the Incremental Conductance method [24,25], the Fractional open circuit voltage method [26,27] and the Fractional short circuit Current method [28,29]. And the artificial intelligence methods Fuzzy logic based MPPT [30] and Neural Networks based ones [31] and hybrid Neuro-fuzzy technique is the objective of this paper.…”