An improved optimization technique for estimation of solar photovoltaic parameters

The nonlinear current vs voltage (I-V) characteristics of solar PV make its modelling difficult. Optimization techniques are the best tool for identifying the parameters of nonlinear models. Even though, there are different optimization techniques used for parameter estimation of solar PV, still the best optimized results are not achieved to date. In this paper, Wind Driven Optimization (WDO) technique is proposed as the new method for identifying the parameters of solar PV. The accuracy and convergence time of the proposed method is compared with results of Pattern Search (PS), Genetic Algorithm (GA), and Simulated Annealing (SA) for single diode and double diode models of solar PV. Furthermore, for performance validation, the parameters obtained through WDO are compared with hybrid Bee Pollinator Flower Pollination Algorithm (BPFPA), Flower Pollination Algorithm (FPA), Generalized Oppositional Teaching Learning Based Optimization (GOTLBO), Artificial Bee Swarm Optimization (ABSO), and Harmony Search (HS). The obtained results clearly reveal that WDO algorithm can provide accurate optimized values with less number of iterations at different environmental conditions. Therefore, the WDO can be recommended as the best optimization algorithm for parameter estimation of solar PV

Improved MPPT algorithm: Artificial neural network trained by an enhanced Gauss-Newton method

A novel approach defined by the artificial neural network (ANN) model trained by the improved Gauss-Newton in conjunction with a simulated annealing technique is used to control a step-up converter. To elucidate the superiority of this innovative method and to show its high precision and speed in achieving the right value of the Maximum Power Point (MPP), a set of three comparative Maximum Power Point Tracker (MPPT) methods (Perturbation and observation, ANN and ANN associated with perturbation and observation) are exanimated judiciously. The behavior of these methods is observed and tested for a fixed temperature and irradiance. As a result, the proposed approach quickly tracks the right MPP = 18.59 W in just 0.04382 s. On the other hand, the outstanding ability of the suggested method is demonstrated by varying the irradiance values (200 W/m2, 300 W/m2, 700 W/m2, 1000 W/m2, 800 W/m2 and 400 W/m2) and by varying the temperature values (15℃, 35℃, 45℃ and 5℃). Therefore, the ANN trained by Gauss-Newton in conjunction with simulated annealing shows a high robustness and achieves the correct value of MPP for each value of irradiance with an efficiency 99.54% and for each value of temperature with an efficiency 99.98%; the three other methods sometimes struggle to achieve the right MPP for certain irradiance values and often remains stuck in its surroundings

Identification of Photovoltaic Cell Model by Neural Network

An accurate parameter identification method of photovoltaic cell model is very helpful to know the behavior of a photovoltaic cell in different meteorological conditions. In this regard, the artificial neural network presents the adequate method that ensures the modeling of the photovoltaic cell characteristic for different values of temperature and irradiance. The present paper presents therefore two neural networks corresponding to the single diode and to the double diodes photovoltaic cell models. Trough the obtained outcomes, the first model provides more accuracy with less complexity of the network unlike the second model entitled under the double diode model, which is more complex and heavy to implement. A further study of the photovoltaic cell behavior is given by the trends of the some obtained electrical parameters according to the investigated temperature and irradiance values

Parameter extraction of photovoltaic module model by using Levenberg-Marquardt algorithm based on simulated annealing method

Abstract Reliable and accurate parameter identification of solar modules model is necessary to evaluate the performances and to control the behavior of photovoltaic systems. In this work, Levenberg-Marquardt combined with simulated annealing algorithm is proposed to extract the five parameters from the experimental data points (IPV - VPV) of Photowatt-PWP 201 polycrystalline module. The quality evaluation of the obtained outcomes is ensured by the analysis of the objective function accuracy. Therefore, the proposed method provides the highest precision compared to the results of the most recent methods reported in the literature. Moreover, a study of the convergence process has been established in the aim to observe the adjustment way of the five parameters and to assess the influence of the damping factor on the objective function optimization. The superiority of the proposed method in terms of accuracy have been tested and proven by using the IPV (VPV)measurements of SM55 mono-crystalline photovoltaic module.</jats:p