State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems – A review
Introduction
The demand for photovoltaic (PV) power generation in power systems and the distribution sector is growing significantly. Research shows that the contribution of PV systems to energy generation was approximately 14,000 MW in 2010 and is expected to be 70,000 MW in 2020 [1]. Australia is a leader in utilizing solar energy resources [2], and had a solar power generation capacity of 115 MW in 2009, which contributed an estimated 0.1–0.2% of the total electricity production. Given the hot, dry, and sunny climate, ideal for solar energy utilization, there is a projected target for 20% of total electricity supply to derive from renewable energy by 2020 [3]. Despite recent advancements in PV utilization-related factors, such as reduction in cost, cell efficiency increases, and improved structural integration to buildings [1], the low energy conversion efficiency of PV systems remains a major impediment to utilization of PV power generation and being able to accurately achieve maximum power point tracking (MPPT) is critically important. Another challenge with PV power generation is the heavy dependence on environmental factors, including solar irradiance and ambient temperature. Therefore, the control unit must be compiled through a capable MPPT technique to harvest the maximum energy from the output terminal of installed PV arrays by providing an appropriate duty cycle to operate the embedded DC–DC converter. Considering all affecting factors such as material efficiency, integration, and structural configuration, boosting the MPPT capability is the most economical way of enhancing the efficiency of the overall PV system [4].
Numerous studies have been undertaken to track the maximum power point (MPP) from the output of PV systems subject to uniform irradiance levels. The perturbation and observation (P&O) [5], [6], [7], [8], [9], hill climbing [10], [11], [12], [13], [14], incremental conductance [15], [16], [17], [18], [19], [20], [21], short-circuit current, open-circuit voltage, and ripple correlation methods are the most popular of conventional approaches to MPP tracking. The main advantages of these methods are the use of a simple structure and fast convergence towards the MPP. These methods however are only able to provide a reliable duty cycle signal when a single MPP exists at the output of the PV system. Using bypass diodes within the circuitry of most of the current PV modules increases the possibility of partial shading conditions (PSCs). The main consequence of these conditions is the occurrence of multiple peaks at the output power–voltage locus. When multiple peaks appear at the output because of PSCs, conventional methods fail to distinguish global MPPs (GMPPs) from local MPPs. The main reason for this failure is that the aforementioned techniques are based on hill-climbing theory, where the operating point being followed shifts in the direction where the output power is maximized [22].
Considering these problems with partial shading, several studies have been performed to modify the performance of conventional methods [23], [24]. These modifications however are achieved through extensive mathematical computation, which can require a powerful and expensive controller. In addition, the modified methods can only track the MPP under a limited number of PSCs. Given the unpredictability of environmental conditions, these methods can be unreliable in tracking the MPP. Different studies have been recently performed where soft computing and artificial intelligence (AI)-based methods were applied to address the detrimental effects of PSCs. The robustness, flexibility, and reliability of soft computing and AI make these methods highly suitable for PSCs. According to the growing number of studies published in this field, a thorough study of recent developments in MPPT methods is essential.
A number of valuable studies have been conducted to review the performance of MPPT methods [4], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34]. Most of these reviews however are limited to discussing the advantages and drawbacks of the approaches without adequate detail to understand the procedures and structures of these methods. This lack of detail in the description is mainly due to the fact that the papers aim to cover all conventional and new approaches. Due to the emergence of PSC problems however, the selection of MPPT techniques requires new considerations so as to make sure they are applicable for environmental conditions typical to partial shading. Thus, most studies use AI-based MPPT methods and validate these methods under different environmental conditions. This paper first focuses on the performance evaluation of recent research relating to six commonly used AI-based techniques capable of MMPT subject to PSCs. To complement the review of these six common techniques, this paper also discusses emerging techniques which despite only having been discussed in a limited number of publications, have demonstrated very good performance in addressing the shortcomings of other MPPT methods under partial shading conditions. The procedures and structures of all the methods are described in sufficient detail to enable researchers and engineers to select the most appropriate MPPT technique.
Section snippets
Normal conditions
Fig. 1 presents a single-diode circuitry for a PV cell. The output of PV systems is directly affected by solar irradiance and temperature. Thus, the latest values of these factors should be employed to obtain the MPP. In addition, the mathematical model of PV changes with the short-circuit current (Isc) and open-circuit voltage (Voc) were obtained from the data sheet provided by the cell manufacturer.
Hence, the generated power of a single solar cell is inadequate for any convenient application.
Theory
Artificial neural network (ANN) is one of the most reputed methods among all soft computing methods that model the operations of biological neural systems. Basically, neural networks are collections of interconnected processing units called neurons, through which signals and information pass. ANN can be considered a mathematical model of a brain-like system that functions as a parallel processing network. This system should undergo an extensive and careful training process to learn how to
Analytical comparison
Published research in this field indicates the difficulty of evaluating and comparing the best MPPT approaches and techniques. In general, the final MPPT technique is selected based on the application requirements and preferences. Therefore, knowledge about the nature of the project and the limitations is an essential prerequisite. In addition, the test benches, applications, and environmental conditions used to verify the performance of the designed MPPT techniques are not similar. Therefore,
Conclusion
In view of the importance of control strategies in the overall efficiency of the PV systems, this paper focused on the different approaches in tracking the MPP of PV systems. Given the drawbacks of conventional MPPT methods, such as system independence, high oscillation around MPP, and deficiency under PSCs, six common and four emerging AI-based MPPT approaches were reviewed in this paper. The concept, structure, sequential steps, and the state of the art of each method of tracking the MPP
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