Comprehensive overview of maximum power point tracking algorithms of PV systems under partial shading condition

This paper proposes an improved maximum power point tracker (MPPT) for photovoltaic (PV) system. The scheme is a hybrid between the adaptive perturb and observe and particle swarm optimization (PSO). The algorithm incorporates the search-skip-judge (SSJ) mechanism to minimize the region within the P−V curve to be searched by the PSO. Furthermore, the PSO performance is enhanced by ensuring that the regions that have been previously explored (by other particle) will not be searched again by (another particle). Thus, the unnecessary movement of particles is minimized—leading to faster convergence. The proposed method is evaluated against four well-known MPPT techniques, namely the modified incremental conductance, the original version of SSJ, the modified cuckoo search, and the hybrid PSO. In addition, an experimental prototype, which is based on PV array simulator is used to verify the simulation. The competing algorithms are tested with a buck-boost converter, driven by the TMS320F240 DSP on the dSPACE DS1104 platform. It was found that the proposed scheme converges to the global maximum power point (GMPP) most rapidly and the GMPP tracking is guaranteed even under complex partial shading conditions.

An Effective Hybrid Maximum Power Point Tracker of Photovoltaic Arrays for Complex Partial Shading Conditions

In the last decade, artificial intelligence (AI) techniques have been extensively used for maximum power point tracking (MPPT) in the solar power system. This is because conventional MPPT techniques are incapable of tracking the global maximum power point (GMPP) under partial shading condition (PSC). The output curve of the power versus voltage for a solar panel has only one GMPP and multiple local maximum power points (MPPs). The integration of AI in MPPT is crucial to guarantee the tracking of GMPP while increasing the overall efficiency and performance of MPPT. The selection of AI-based MPPT techniques is complicated because each technique has its own merits and demerits. In general, all of the AI-based MPPT techniques exhibit fast convergence speed, less steady-state oscillation and high efficiency, compared with the conventional MPPT techniques. However, the AI-based MPPT techniques are computationally intensive and costly to realize. Overall, the hybrid MPPT is favorable in terms of the balance between performance and complexity, and it combines the advantages of conventional and AI-based MPPT techniques. In this paper, a detailed comparison of classification and performance between 6 major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results. The merits, open issues and technical implementations of AI-based MPPT techniques are evaluated. We intend to provide new insights into the choice of optimal AI-based MPPT techniques.

/pdf/artificial-intelligence-based-mppt-techniques-for-solar-122xph2f0n.pdf

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

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

The primary concerns in the practical photovoltaic (PV) system are the power reduction due to the change in operating conditions, such as the temperature or irradiance, the high computation burden due to the modern maximum power point tracking (MPPT) mechanisms, and to maximize the PV array output during the rapid change in weather conditions. The conventional perturb and observation (P&O) technique is preferred in most of the PV systems. Nevertheless, it undergoes false tracking of maximum power point (MPP) during the rapid change in solar insolation due to the wrong decision in the duty cycle. To avoid the computational burden and drift effect, this article presents a simple and enhanced P&O MPPT technique. The proposed technique is enhanced by including the change in current ( dI ), in addition to the changes in output voltage and output power of the PV module. The effect of including the dI profile with the traditional method is explained with the fixed and variable step-size methods. The mathematical expression for the drift-free condition is derived. The traditional boost converter is considered for validating the effectiveness of the proposed methods by employing the direct duty cycle technique. The proposed algorithm is simulated using MATLAB/Simulink and validated under various scenarios with the developed laboratory prototype in terms of drift-free characteristics and tracking efficiency. The result proves that the proposed technique can track the MPP accurately under various operating conditions.

Improved Perturb and Observation Maximum Power Point Tracking Technique for Solar Photovoltaic Power Generation Systems

In this study, a novel and fast maximum power point tracking (MPPT) algorithm for a photovoltaic generation system is proposed. The main idea is to remove the random number in the voltage calculation equation of the conventional cuckoo search method. The advantages of the proposed method include 1) fast maximum power point (MPP) tracking speed and high MPP tracking accuracy under both uniform insolation and partially shaded conditions (PSCs), 2) simple MPPT structure, and 3) consistent solution can be achieved with only three particles and only one parameter is required to be tuned. In order to validate the effectiveness and correctness of the proposed method, both simulation and experiments are carried out on a 300 W prototyping circuit. According to the experimental results, the proposed MPPT method can improve the tracking time by 46.42% and 11.76% comparing to conventional perturb and observe (P&O) and variable-step P&O technique, respectively. In addition, the proposed method can successfully track the global MPP in 249 of 252 different PSC patterns. The tracking accuracies under both uniform irradiance and PSC conditions are all higher than 99.8%.

Kun-Che Ho

Papers

A Novel and Fast MPPT Method Suitable for Both Fast Changing and Partially Shaded Conditions