scholarly journals An Enhanced Incremental Conductance Algorithm for Photovoltaic System

2018 ◽  
Vol 22 (1) ◽  
pp. 12
Author(s):  
K. Ramesh ◽  
R. Giri Ganesh ◽  
Sri Lakshmi Vineela Reddy ◽  
K. Mahalakshmi ◽  
S. Suganya

The energy obtained from the photovoltaic array is dependent on the available solar insolation, the panel tilt angle and the power point tracking algorithm of the system. Some of the Conventional MPPT methods are developed by considering uniform solar irradiance. During partial shading conditions, solar panel may produce multiple Local Maximum Power Points (LMPPs) in its power voltage characteristic curve. A new algorithm has been developed in this paper by using sequential sampling embedded with existing incremental conductance procedure in order to predict the Global Maximum Power Point (GMPP). The tracking capability of proposed algorithm is verified with simulation works carried out in MATLAB/SIMULINK. The results of proposed algorithm are likened with the results classical Perturb and Observe (P&O) and Incremental Conductance algorithms.


Author(s):  
Salmi Hassan ◽  
Badri Abdelmajid ◽  
Zegrari Mourad ◽  
Sahel Aicha ◽  
Baghdad Abdenaceur

<p>Maximum power point tracking (MPPT) algorithms are employed in photovoltaic (PV) systems to make full utilization of PV array output power, which have a complex relationship between ambient temperature and solar irradiation. The power-voltage characteristic of PV array operating under partial shading conditions (PSC) exhibits multiple local maximum power points (LMPP). In this paper, an advanced algorithm has been presented to track the global maximum power point (GMPP) of PV. Compared with the Perturb and Observe (P&amp;O) techniques, the algorithm proposed the advantages of determining the location of GMPP whether partial shading is present.</p>



Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 327 ◽  
Author(s):  
Muhammad Afzal Awan ◽  
Tahir Mahmood

Optimal energy extraction under partial shading conditions from a photovoltaic (PV) array is particularly challenging. Conventional techniques fail to achieve the global maximum power point (GMPP) under such conditions, while soft computing techniques have provided better results. The main contribution of this paper is to devise an algorithm to track the GMPP accurately and efficiently. For this purpose, a ten check (TC) algorithm was proposed. The effectiveness of this algorithm was tested with different shading patterns. Results were compared with the top conventional algorithm perturb and observe (P&O) and the best soft computing technique flower pollination algorithm (FPA). It was found that the proposed algorithm outperformed them. Analysis demonstrated that the devised algorithm achieved the GMPP efficiently and accurately as compared to the P&O and the FPA algorithms. Simulations were performed in MATLAB/Simulink.



Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4775
Author(s):  
Kuei-Hsiang Chao ◽  
Yu-Ju Lai

In this study, a maximum power point tracker was developed for photovoltaic module arrays by using a teacher-learning-based optimization (TLBO) algorithm to control the photovoltaic system. When a photovoltaic module array is shaded, a conventional maximum power point tracker may obtain the local maximum power point rather than the global maximum power point. The tracker developed in this study was aimed at solving this problem. To prove the viability of the proposed method, a SANYO HIP 2717 photovoltaic module with diverse connection patterns and shading ratios was used. Thus, single-peak, double-peak, triple-peak, and multi-peak power–voltage characteristic curves of the photovoltaic module array were obtained. A simulation of maximum power point tracking (MPPT) was then performed with MATLAB software. With regard to practical testing, a boost converter was used as the hardware structure of the maximum power point tracker and a TMS320F2808 digital signal processor was selected to execute the rules for MPPT. The results of the practical tests verified that the proposed improved TLBO algorithm had a superior accuracy to existing TLBO algorithms. In addition, the proposed improved TLBO algorithm can shorten the tracking time to 1/2 or 1/4, so it can improve the efficiency of power generation by two to three percentage.



Author(s):  
Bennis Ghita ◽  
Karim Mohammed ◽  
Lagrioui Ahmed

Several algorithms have been offered to track the Maximum Power Point when we have one maximum power point. Moreover, fuzzy control and neural was utilized to track the Maximum Power Point when we have multi-peaks power points. In this paper, we will propose an improved Maximum Power Point tracking method for the photovoltaic system utilizing a modified PSO algorithm. The main advantage of the method is the decreasing of the steady state oscillation (to practically zero) once the Maximum Power Point is located. moreover, the proposed method has the ability to track the Maximum Power Point for the extreme environmental condition that cause the presence of maximum multi-power points, for example, partial shading condition and large fluctuations of insolation. To evaluate the effectiveness of the proposed method, MATLAB simulations are carried out under very challenging circumstance, namely step changes in irradiance, step changes in load, and partial shading of the Photovoltaic array. Finally, its performance is compared with the perturbation and observation” and fuzzy logic results for the single peak, and the neural-fuzzy control results for the multi-peaks.



2020 ◽  
Vol 12 (24) ◽  
pp. 10310 ◽  
Author(s):  
Abdulaziz Almutairi ◽  
Ahmed G. Abo-Khalil ◽  
Khairy Sayed ◽  
Naif Albagami

The disadvantage of photovoltaic (PV) power generation is that output power decreases due to the presence of clouds or shade. Moreover, it can only be used when the sun is shining. Consequently, there is a need for further active research into the maximum power point tracking (MPPT) technique, which can maximize the power of solar cells. When the solar cell array is partially shaded due to the influence of clouds or buildings, the solar cell characteristic has a number of local maximum power points (LMPPs). Conventional MPPT techniques do not follow the actual maximum power point, namely, the global maximum power point (GMPP), but stay in the LMPP. Therefore, an analysis of the occurrence of multiple LMPPs due to partial shading, as well as a study on the MPPT technique that can trace GMPP, is needed. In order to overcome this obstacle, the grey wolf optimization (GWO) method is proposed in order to track the global maximum power point and to maximize the energy extraction of the PV system. In addition, opposition-based learning is integrated with the GWO to accelerate the MPPT search process and to reduce convergence time. Simultaneously, the DC link voltage is controlled to reduce sudden variations in voltage in the event of transients of solar radiation and/or temperature. Experimental tests are presented to validate the effectiveness of the proposed MPPT method during uniform irradiance and partial shading conditions. The proposed method is compared with the perturbation and observation method.





2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Saad Motahhir ◽  
Abdelaziz El Ghzizal ◽  
Souad Sebti ◽  
Aziz Derouich

The first objective of this work is to determine some of the performance parameters characterizing the behavior of a particular photovoltaic (PV) panels that are not normally provided in the manufacturers’ specifications. These provide the basis for developing a simple model for the electrical behavior of the PV panel. Next, using this model, the effects of varying solar irradiation, temperature, series and shunt resistances, and partial shading on the output of the PV panel are presented. In addition, the PV panel model is used to configure a large photovoltaic array. Next, a boost converter for the PV panel is designed. This converter is put between the panel and the load in order to control it by means of a maximum power point tracking (MPPT) controller. The MPPT used is based on incremental conductance (INC), and it is demonstrated here that this technique does not respond accurately when solar irradiation is increased. To investigate this, a modified incremental conductance technique is presented in this paper. It is shown that this system does respond accurately and reduces the steady-state oscillations when solar irradiation is increased. Finally, simulations of the conventional and modified algorithm are compared, and the results show that the modified algorithm provides an accurate response to a sudden increase in solar irradiation.



2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Kok Soon Tey ◽  
Saad Mekhilef ◽  
Hong-Tzer Yang ◽  
Ming-Kai Chuang

Partially shaded photovoltaic (PV) modules have multiple peaks in the power-voltage(P-V)characteristic curve and conventional maximum power point tracking (MPPT) algorithm, such as perturbation and observation (P&O), which is unable to track the global maximum power point (GMPP) accurately due to its localized search space. Therefore, this paper proposes a differential evolution (DE) based optimization algorithm to provide the globalized search space to track the GMPP. The direction of mutation in the DE algorithm is modified to ensure that the mutation always converges to the best solution among all the particles in the generation. This helps to provide the rapid convergence of the algorithm. Simulation of the proposed PV system is carried out in PSIM and the results are compared to P&O algorithm. In the hardware implementation, a high step-up DC-DC converter is employed to verify the proposed algorithm experimentally on partial shading conditions, load variation, and solar intensity variation. The experimental results show that the proposed algorithm is able to converge to the GMPP within 1.2 seconds with higher efficiency than P&O.



2016 ◽  
Vol 2016 ◽  
pp. 1-19 ◽  
Author(s):  
Ru-Min Chao ◽  
Ahmad Nasirudin ◽  
I-Kai Wang ◽  
Po-Lung Chen

This paper identifies the partial shading problem of a PV module using the one-diode model and simulating the characteristics exhibiting multiple-peak power output condition that is similar to a PV array. A modified particle swarm optimization (PSO) algorithm based on the suggested search-agent deployment, retracking condition, and multicore operation is proposed in order to continuously locate the global maximum power point for the PV system. Partial shading simulation results for up to 16 modules in series/parallel formats are presented. A distributed PV system consisting of up to 8 a-silicon thin film PV panels and also having a dedicated DC/DC buck converter on each of the modules is tested. The converter reaches its steady state voltage output in 10 ms. However for MPPT operation, voltage, and current measurement interval is set to 20 ms to avoid unnecessary noise from the entire electric circuit. Based on the simulation and experiment results, each core of the proposed PSO operation should control no more than 4 PV modules in order to have the maximum tracking accuracy and minimum overall tracking time. Tracking for the global maximum power point of a distributed PV system under various partial shading conditions can be done within 1.3 seconds.



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