A Novel Improved Cuckoo Search Algorithm for Parameter Estimation of Photovoltaic (PV) Models

Tong Kang,Min Jin,Shengjie Yang,Thanhlong Duong,Jiangang Yao

doi:10.3390/en11051060

Abstract

Parameter estimation of photovoltaic (PV) models from experimental current versus voltage (I-V) characteristic curves acts a pivotal part in the modeling a PV system and optimizing its performance. Although many methods have been proposed for solving this PV model parameter estimation problem, it is still challenging to determine highly accurate and reliable solutions. In this paper, this problem is firstly transformed into an optimization problem, and an objective function (OF) is formulated to quantify the overall difference between the experimental and simulated current data. And then, to enhance the performance of original cuckoo search algorithm (CSA), a novel improved cuckoo search algorithm (ImCSA) is proposed, by combining three strategies with CSA. In ImCSA, a quasi-opposition based learning (QOBL) scheme is employed in the population initialization step of CSA. Moreover, a dynamic adaptation strategy is developed and introduced for the step size without Lévy flight step in original CSA. A dynamic adjustment mechanism for the fraction probability (Pa) is proposed to achieve better tradeoff between the exploration and exploitation to increase searching ability. Afterwards, the proposed ImCSA is used for solving the problem of estimating parameters of PV models based on experimental I-V data. Finally, the proposed ImCSA has been demonstrated on the parameter identification of various PV models, i.e., single diode model (SDM), double diode model (DDM) and PV module model (PMM). Experimental results indicate that the proposed ImCSA outperforms the original CSA and its superior performance in comparison with other state-of-the-art algorithms, and they also show that our proposed ImCSA is capable of finding the best values of parameters for the PV models in such effective way for giving the best possible approximation to the experimental I-V data of real PV cells and modules. Therefore, the proposed ImCSA can be considered as a promising alternative to accurately and reliably estimate parameters of PV models.

Highlights

In recent years, several reasons such as gradually depleting fossil fuel resources, environmental protection concerns, and political issues have resulted in a high demand for electrical energy [1].the conflict between the vigorously increasing power demands and scarcity of fossil resource is becoming more and more serious, promoting the development of renewable energy resources, Energies 2018, 11, 1060; doi:10.3390/en11051060 www.mdpi.com/journal/energiesEnergies 2018, 11, 1060 especially solar energy [2,3]
A dynamic adaptation strategy is developed and introduced for the step size without Lévy flight step in original cuckoo search algorithm (CSA), which makes the step size with zero parameter initialization adaptively change according to the individual nest’s fitness value over the course of the iteration and the current iteration number. This strategy is useful for optimization with a faster rate
PV module are chosen to verify the effectiveness of proposed improved cuckoo search algorithm (ImCSA) and compare with the results reported in literature

Summary

Introduction

Several reasons such as gradually depleting fossil fuel resources, environmental protection concerns, and political issues have resulted in a high demand for electrical energy [1].the conflict between the vigorously increasing power demands and scarcity of fossil resource is becoming more and more serious, promoting the development of renewable energy resources, Energies 2018, 11, 1060; doi:10.3390/en11051060 www.mdpi.com/journal/energiesEnergies 2018, 11, 1060 especially solar energy [2,3]. Since solar energy is emission-free, freely available, and easy to install, the use of solar energy via photovoltaic (PV) systems has attracted great attention all over the world [4,5]. Energy Agency (IEA PVPS) [6], the global solar PV capacity at the end of 2016 amounted to about. Namely China, USA and Japan represented the largest solar PV markets in 2015 as well as 2016, in which there was a 75% increase in newly installed solar PV capacity. Many countries were increasing their installed PV capacity during 2016, which is still going on. In PV systems, solar PV cells or modules are applied for harnessing the Sun’s energy and turn it into electricity. With regard to the modeling a PV system and optimizing its performance, an accurate modeling of PV cells or modules is necessary

Methods

Results

Conclusion