Identification of one-diode model parameters of PV devices from nameplate information using particle swarm and least square methods

Abstract

The paper proposes a simple method to estimate the parameters of a one-diode model of PV modules based on particle swarm optimization (PSO) and least square (LS) algorithms. The employed method requires only the PV module nameplate ratings at three points on the I–V curve at Standard Test Conditions: short circuit current, open circuit voltage and maximum power (V MPP , I MPP ). The method is verified on two different types of PV modules (poly-crystalline and monocrystalline). A comparison between actual experimental data curves (obtained from manufacturer data sheets) and the predicted PV characteristics is carried out for different operating conditions. The PSO algorithm has been tested 100 times to determine the average value of the model parameters. The corresponding relative percentage error between measured and predicted PV electrical parameters did not exceed 8 % in the worst case, while the elapsed CPU time of the PSO algorithm was less than 7 sec. The results proved the validity and effectiveness of the method to identify with good accuracy the parameters of a PV module using only the limited datasheet information. It has been observed that the LS method can fail to find a solution without a proper selection of the initial values. The limitation of the LS method has been overcome by utilizing the final PSO solution as initial condition for the LS nonlinear curve fitting algorithm. With the LS search method (initialized by PSO results) accurate solution is also obtained. So, the proposed parameter estimation approach has been validated by two techniques PSO and LS. The main contribution of this approach is its suitability for rapid prototyping of PV systems with a high precision without the need to install an experimental test rig.