Elsevier

Solar Energy

Volume 183, 1 May 2019, Pages 476-500
Solar Energy

Comprehensive review on global maximum power point tracking techniques for PV systems subjected to partial shading conditions

https://doi.org/10.1016/j.solener.2019.03.045Get rights and content

Abstract

Under partial shading conditions, photovoltaic (PV) arrays exhibit multiple local maximum power points and a single global maximum power point in the P-V characteristics curves. Therefore, efficient global maximum power point tracking (GMPPT) control techniques are the prerequisite for improving the performance of the PV array under such situations. In the literature, various GMPPT techniques have been developed to track accurately the global maximum power point among different local maxima. The objective of this study is to provide a review on recent GMPPT techniques for users to select the most appropriate method based on application requirements and system specifications. The proposed work classifies and analyzes these new strategies that are used to maximize the output power of the photovoltaic system and presents the advantages and disadvantages of each method. In addition, it gives views on the use of each method, which helps to choose the most appropriate method according to the PV application. This work will serve as a useful reference for researchers working in the field.

Introduction

A photovoltaic module has nonlinear P-V characteristics curves, which vary with environmental conditions such as temperature and solar irradiance. To extract the maximum power available from the PV module at any time, maximum power point tracking (MPPT) control techniques are vital. Traditional strategies such as perturb and observe (P&O), incremental conductance (IC) and hill climb (HC) are usually used to track maximum power under uniform irradiance conditions (Femia et al., 2005, Esram and Chapman, 2007). However, these methods cannot deal with the problem of partial shading, which induces multiple peaks caused by the insertion of bypass diodes. Therefore, partial shading is a serious problem that causes significant power losses which decreases the efficiency of the PV system (Ahmed and Miyatake, 2008, Koutroulis and Blaabjerg, 2012, Renaudineau et al., 2011). To solve this problem, several solutions are offered to maximize the output power of the PV system (Bidram et al., 2012, Belhachat and Larbes, 2015, Belhachat and Larbes, 2017). In this work, we are only interested on tracking techniques of the global maximum power point under partial shading conditions.

A variety of MPPT methods for maximum power point tracking under partial shading conditions have been developed and can be found in literature reviews such as (Belhachat and Larbes, 2018, Lian et al., 2018, Danandeh and Mousavi, 2018, Li et al., 2018a, Makbul et al., 2017, Joshi and Arora, 2017, Mohapatra et al., 2017, Batarseh and Za'ter, 2018, Gupta et al., 2016, Dileep and Singh, 2017).

For instance, in (Belhachat and Larbes, 2018) the GMPPT techniques were categorized as methods based on optimization algorithm, hybrid algorithm, mathematical model and other GMPPT.

In addition to that, other methods have been recently developed to track the GMPP under non-uniform conditions. Among these strategies, those involving the application of intelligent and metaheuristic search algorithms. We can mention improved chicken swarm optimization (ICSO) (Wu et al., 2018), modified cat swarm optimization (MCSO) (Guo et al., 2018), improved gravitational search algorithm (IGSA) (Ling et al., 2018), improved differential evolution algorithm (IDE) (Tey et al., 2018), modified particle velocity-based particle swarm optimization (MPV-PSO) (Sen et al., 2018), modified firefly algorithm (MFA) (Farzaneh et al., 2018), deterministic cuckoo search algorithm (DCS) (Peng et al., 2018), moth-flame optimization algorithm (MFO) (Aouchiche et al., 2018), artificial mountain ape optimization (Gupta and Saurabh, 2017a), modified artificial killer whale optimization algorithm (MAKWO) (Gupta and Saurabh, 2017b). Further, hybrid strategies have been proposed to improve the performance of the above methods such as hybrid grey wolf optimization and beta method (GWO-Beta) (Rocha et al., 2018), modified genetic algorithm and firefly algorithm (MGA-FA) (Huang et al., 2018), single current sensor hill climbing and artificial bee colony (SSHC-ABC) algorithms (Saikrishna et al., 2018), grey wolf optimization and golden-section optimization (GWO-GSO) (Shi et al., 2018), overall distribution and PSO algorithm (OD-PSO) (H. Li et al., 2018), particle swarm optimization combined with one cycle control (PSO-OCC) (Anoop and Nandakumar, 2018), method based on ANN assisted sequential Monte Carlo and quickest change detection (ANN-SMC) (Chen and Wang, 2018), improved 0.8 VOC model and P&O algorithm (Başoğlu and Çakır, 2018), hybrid taguchi genetic algorithm (HTGA) (Lee et al., 2018), artificial neural network and particle swarm optimization algorithm (ANN-PSO) (Said et al., 2017), hybrid particle swarm optimization gravitational search algorithm (PSO-GSA) (Mohamed et al., 2019), gray wolf optimization and fuzzy logic controller (GWO-FLC) (Eltamaly and Farh, 2019), hybrid Gaussian process regression-Jaya (GPR-Jaya) algorithm (Chao et al., 2019).

Other GMPPT methods have been used for the same objective, these methods include approximating I-V curve (Ghasemi et al., 2018), current source region detection of I-V curve (Aquib and Jain, 2018), maximum power trapezium (MPT) algorithm (Furtado et al., 2018), a power-increment algorithm (X. Li et al., 2018), two stage using searching technique and bisection method (Ramana et al., 2018), enhanced Bayesian based MPPT (Keyrouz, 2018), high speed MPPT module (Sankar et al., 2018), enhanced adaptive P&O (EA-P&O) (Ahmed and Salam, 2018), artificial vision algorithm (Martin et al., 2018), a bypass diode scanning approach (Winston et al., 2018), method based on maximum power that the series and parallel configuration of PV cells are able to deliver (Ashouri-Zadeh et al., 2018)…etc. All the above mentioned methods will be detailed in this paper.

As mentioned above, there are several MPPT techniques dealing with partial shading conditions cases in the literature. However, for a designer it is difficult to make the right choice for a specific application. Each application has its own criteria like efficiency, tracking speed, cost, accuracy, implementation complexity etc.

For this purpose, this study reviews the literature on recent MPPT methods under partial shading conditions. The MPPT methods can be classified in different ways on the basis of the criteria used to track the MPP. In this paper, the reviewed MPPT methods are classified into four major groups and compared. The first group includes optimization MPPT techniques. The second category consists of different hybrids techniques. The third category includes the techniques based on exploitation of characteristics curves and the fourth category deals with other global MPPT techniques.

The rest of the paper has the following organization. 2 Characteristics of PV array under partial shading conditions, 3 DC–DC converter topology describe the characteristics of PV array under partial shading conditions and the DC-DC converter topologies, respectively. Section 4 details the selected global MPPT techniques to track GMPP. Section 5 analyzes and discusses reviewed and selected algorithms. Finally, a conclusion is given in Section 6.

Section snippets

Characteristics of PV array under partial shading conditions

As discussed above, in the presence of partial shading, several peaks appear on the characteristic curve of PV array.

Fig. 1 illustrates the Simulink model of the 4 × 4 PV array configuration connected in series. The corresponding irradiance level (shading pattern) for each PV module is depicted in Table 1.

Fig. 2, Fig. 3 show the P-V characteristics of 4x4 PV array connected in series under uniform conditions (1000 W/m2) and partial shading conditions for different temperature levels,

DC–DC converter topology

The DC-DC power converter is the important element in PV system’s optimization, as it acts as an interface between the PV generator and the adapted load (Ba et al., 2018). This DC-DC power converter is controlled through its switch by a signal called the duty cycle which is generated by the MPPT technique.

In general, the duty cycles given to the switch decreases or increases the voltage produced by the PV generator so that it can converges to the MPP voltage, by using a voltage step-down (buck)

GMPPT methods

There are different MPPT techniques available for extracting maximum power from PV arrays. Some of the important techniques are discussed below.

The selected methods can be categorized into four main groups. The first group includes optimization algorithms based MPPT techniques. The second category includes different hybrid algorithms based MPPT. The third category includes methods that exploit characteristics curves of the PV array. The fourth category is classified as other GMPPT.

Comparison and discussion

In the previous sections, the methods developed in the literature for the PSC cases have been classified and evaluated with their advantages and drawbacks. In this section, Table 2 aims to give a brief evaluation of the selected studies to the designers. In Table 2, all studies of this review were evaluated from the point of view of complexity level, tracking speed, efficiency, and complexity level, etc.

Through the analysis of the different methods, the following remarks can be made:

  • The

Conclusion

In the literature, there is wide variety of works dealing with tracking the global MPP under partial shading conditions. Therefore, it is extremely difficult for the designer to determine the suitable method given the large number of techniques. For this purpose, this review study has discussed and analyzed the most important and recent techniques developed in the literature, revealing the advantages and disadvantages of each technique. In this study, the selected techniques were classified

Conflict of interest

The authors declare that they have no conflict of interest.

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