scholarly journals Maximum Power Point Tracking for a PV System Using Tuned Support Vector Regression by Particle Swarm Optimization

2020 ◽  
Vol 8 (4) ◽  
Author(s):  
Ahmed G. Abo-Khalil ◽  
◽  
Keyword(s):  
Support Vector Regression ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Solar Irradiation ◽  
Support Vector ◽  
Pv System ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

The photovoltaic (PV) system is always operated at the maximum power point (MPP) condition irrespective of the fluctuations in PV voltage. The maximum power point tracking (MPPT) employed in PV system is not effective during the presence of current ripple as normal tracking becomes increasingly complex during fluctuation in solar irradiation or due to change in MPP condition. This paper proposes a high-efficiency power point tracking algorithm to minimize the current ripple and power oscillation around the maximum power point. The developed algorithm is based on particle swarm optimization-support vector regression (PSO-SVR) technique. The proposed algorithm is implemented to select and tune the Support Vector Regression (SVR) parameters such as kernel parameters, variance, and the penalty factor for predicting the irradiation level as well as to determine the PV voltage corresponding of maximum power point. The PSO method is used to accelerate the process of optimizing the SVR parameters at different conditions and get knowledge about the corresponding global optimum. From the experimental results,the efficiency of maximum power point tracking is found to be 99.8%. The proposed algorithm PSO-SVR shows a better performance than using SVR alone. The stability and accuracy of MPPT have been validated during the rapid fluctuation of solar irradiation in the range of 25% to 100%.

Maximum Power Point Tracking Based on SVR and GA

2014 ◽  
Vol 687-691 ◽  
pp. 3231-3234
Author(s):  
Zhi Guang Tian ◽  
Lin Tian ◽  
Jian He ◽  
Zhen Hua Huang ◽  
Da Hai Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Support Vector ◽  
Pv System ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point ◽  
Best Parameters

With the increasing application of Photovoltaic (PV) power system, it is important to make PV system always achieve its maximum power output, so maximum power point tracking (MPPT) technique develops. Based on Support Vector Regression (SVR) and Genetic Algorithm (GA), a novel MPPT method is proposed in this paper. The SVR model uses the solar radiation and temperature as two inputs, and uses the voltage at maximum power point (MPP) as output. Furthermore, GA is introduced to search the best parameters for SVR. Results validate the effectiveness of the proposed MPPT method.

scholarly journals Maximum Power Point Tracking Based on Sliding Mode Control

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Nimrod Vázquez ◽  
Yuz Azaf ◽  
Ilse Cervantes ◽  
Eslí Vázquez ◽  
Claudia Hernández
Keyword(s):  
Sliding Mode ◽  
Maximum Power Point Tracking ◽  
Good Choice ◽  
Maximum Power ◽  
Solar Irradiation ◽  
Pv System ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

Solar panels, which have become a good choice, are used to generate and supply electricity in commercial and residential applications. This generated power starts with the solar cells, which have a complex relationship between solar irradiation, temperature, and output power. For this reason a tracking of the maximum power point is required. Traditionally, this has been made by considering just current and voltage conditions at the photovoltaic panel; however, temperature also influences the process. In this paper the voltage, current, and temperature in the PV system are considered to be a part of a sliding surface for the proposed maximum power point tracking; this means a sliding mode controller is applied. Obtained results gave a good dynamic response, as a difference from traditional schemes, which are only based on computational algorithms. A traditional algorithm based on MPPT was added in order to assure a low steady state error.

Application of a Chaotic Quantum Bee Colony and Support Vector Regression to Multipeak Maximum Power Point Tracking Control Method Under Partial Shading Conditions

2020 ◽  
Vol 19 (02) ◽  
pp. 2050014
Author(s):  
Xiangming Gao ◽  
Diankuan Ding ◽  
Shifeng Yang ◽  
Mingkun Huang
Keyword(s):  
Support Vector Regression ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Support Vector ◽  
Maximum Power Point ◽  
Photovoltaic Array ◽  
Point Tracking ◽  
Bee Colony ◽  
Power Point Tracking ◽  
Power Point

In view of the multipeak characteristics of a photovoltaic (PV) array P–V curve under local shadow conditions and that the traditional maximum power point tracking (MPPT) algorithm cannot effectively track the maximum power point of the curve, a multipeak MPPT algorithm based on a chaotic quantum bee colony and support vector regression (SVR) is proposed. By constructing and analyzing the mathematical model of a photovoltaic array under a local shadow, the P–V characteristic equation of the photovoltaic array is obtained. The improved strategy of the artificial bee colony algorithm is studied, and the improved chaotic quantum bee colony algorithm (CQABC) is applied to the optimization of SVR parameters; this application improves the accuracy and generalization performance of the maximum power point prediction model based on SVR. The calculation process of the multipeak MPPT algorithm based on CQABC-SVR is given, and the effectiveness of the algorithm is verified by simulation and testing. The experimental results show that the algorithm can accurately track the global maximum power point under uniform illumination or local shadow conditions, effectively overcoming the problem of traditional MPPT algorithms easily falling into local extrema.

scholarly journals Review of Online and Soft Computing Maximum Power Point Tracking Techniques under Non-Uniform Solar Irradiation Conditions

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3256 ◽  
Author(s):  
Amjad Ali ◽  
K. Almutairi ◽  
Muhammad Zeeshan Malik ◽  
Kashif Irshad ◽  
Vineet Tirth ◽  
...  
Keyword(s):  
Soft Computing ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Solar Irradiation ◽  
Pv System ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Review Articles ◽  
Power Point

Significant growth in solar photovoltaic (PV) installation has been observed during the last decade in standalone and grid-connected power generation systems. However, the PV system has a non-linear output characteristic because of weather intermittency, which tends to a substantial loss in overall system output. Thus, to optimize the output of the PV system, maximum power point tracking (MPPT) techniques are used to track the global maximum power point (GMPP) and extract the maximum power from the PV system under different weather conditions with better precision. Since MPPT is an essential part of the PV system, to date, many MPPT methods have been developed by various researchers, each with unique features. A Google Scholar survey of the last five years (2015–2020) was performed to investigate the number of review articles published. It was found that overall, seventy-one review articles were published on different MPPT techniques; out of those, only four were on non-uniform solar irradiance, and seven review articles included shading conditions. Unfortunately, very few attempts were made in this regard. Therefore, a comprehensive review paper on this topic is needed, in which almost all the well-known MPPT techniques should be encapsulated in one document. This article focuses on online and soft-computing MPPT algorithm classifications under non-uniform irradiance conditions along with their mathematical expression, operating principles, and block diagram/flow charts. It will provide a direction for future research and development in the field of maximum power point tracking optimization.

scholarly journals Improved Perturb and Observation Method Based on Support Vector Regression

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1151 ◽  
Author(s):  
Bicheng Tan ◽  
Xin Ke ◽  
Dachuan Tang ◽  
Sheng Yin
Keyword(s):  
Support Vector Regression ◽  
Direct Current ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Support Vector ◽  
Tracking Accuracy ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

Solar energy is the most valuable renewable energy source due to its abundant storage and is pollution-free. The output power of photovoltaic (PV) arrays will vary with external conditions, such as irradiance and temperature fluctuations. Therefore, an increase in the energy conversion rate is inseparable from maximum power point tracking (MPPT). The existing MPPT technology cannot either balance the tracking speed and tracking accuracy, or the implementation cost is too high due to the complexity of the calculation. In this paper, a new maximum power point tracking (MPPT) method was proposed. It improves the traditional perturb and observation (P&O) method by introducing the support vector regression (SVR) algorithm. In this method, the current maximum power point voltage is predicted by the trained model and compared with the current operating voltage to predict a reasonable step size. The boost DC/ DC (Direct current-Direct current converter) convert system applying the improved method and the traditional P&O was simulated in MATLAB-Simulink, respectively. The results of the simulation show that compared with the traditional P&O method, the proposed new method both improves the convergence time and tracking accuracy.

Assessing Under Varying Circumstances the Behavior of Photovoltaic Systems of Maximum Power Point Methods.

2019 ◽  
Vol 1 (2) ◽  
pp. 45-51
Author(s):  
Imad A. Elzein ◽  
Yuri N. Petrenko
Keyword(s):  
Comparative Studies ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Pv System ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
System Output ◽  
Power Point ◽  
Design Simplicity

In this article an extended literature surveying review is conducted on a set of comparative studies of maximum power point tracking (MPPT) techniques.  Different MPPT methods are conducted with an ultimate aim of how to be maximizing the PV system output power by tracking Pmax in a set of different operational circumstances. In this paper maximum power point tracking, MPPT techniques are reviewed on basis of different parameters related to the design simplicity and or complexity, implementation, hardware required, and other related aspects.

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