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.

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 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.

Maximum power point tracking control of wind energy conversion system driving a permanent magnet synchronous generator: Comparative study

2020 ◽  
pp. 0309524X2094438
Author(s):  
Omessaad Elbeji ◽  
Marwa Hannachi ◽  
Mouna Benhamed ◽  
Lassaad Sbita
Keyword(s):  
Wind Energy ◽  
Direct Current ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Wind Energy Conversion System ◽  
Wind Energy Conversion ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

A wind energy conversion system needs a maximum power point tracking strategy. In the literature, several works have interested in the search for a maximum power point. Generally, their goals are to optimize the rotation speed or the machine torque and the direct current–direct current or the alternating current–direct current duty cycle switchers. This work presents a comparative study between two maximum power point tracking strategies of a wind energy conversion system. The model of the system is studied and developed. It includes a permanent magnet synchronous generator, a diode rectifier and a three-cell direct current–direct current converter. The direct current–direct current is controlled in order to generate the wind maximum power using the tip speed ratio strategy and optimal torque strategy. The effectiveness of the used strategies control scheme is proved by simulation results using MATLAB/Simulink.

Comparison of Methods of Maximum Power Point Tracking of Photovoltaic Cells

2013 ◽  
Vol 380-384 ◽  
pp. 3362-3365
Author(s):  
Lan Li ◽  
Yong Hui He ◽  
Bo Wang
Keyword(s):  
Control System ◽  
Maximum Power Point Tracking ◽  
Photovoltaic Cells ◽  
Maximum Power ◽  
Tracking Accuracy ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point ◽  
Observation Method

According to engineering mathematics model of solar photovoltaic cells, a simulation model of photovoltaic cells was established in Matlab. In view of problem that it is difficult to get higher tracking accuracy and response speed by use of perturbation and observation method which applied fixed perturbation step, the paper proposed an improved perturbation and observation method based on variable step. Through simulating photovoltaic cells control system, simulation curves of two kinds of methods of maximum power point tracking were compared. The simulation results show that the photovoltaic cells control system can track maximum power point more quickly and has better stability at the maximum power point by use of the improved perturbation and observation method.

scholarly journals Assessing Maximum Power Point Tracking Intelligent Techniques on a PV System with a Buck–Boost Converter

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7453
Author(s):  
Maria I. S. Guerra ◽  
Fábio M. Ugulino de Araújo ◽  
Mahmoud Dhimish ◽  
Romênia G. Vieira
Keyword(s):  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Ambient Conditions ◽  
Tracking Accuracy ◽  
Pv System ◽  
Maximum Power Point ◽  
Pv Systems ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

Classic and intelligent techniques aim to locate and track the maximum power point of photovoltaic (PV) systems, such as perturb and observe (P&O), fuzzy logic (FL), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFISs). This paper proposes and compares three intelligent algorithms for maximum power point tracking (MPPT) control, specifically fuzzy, ANN, and ANFIS. The modeling of a single-diode equivalent circuit-based 3 kWp PV plant was developed and validated to achieve this purpose. Then, the MPPT techniques were designed and applied to control the buck–boost converter’s switching device of the PV plant. All three methods use the ambient conditions as input variables: solar irradiance and ambient temperature. The proposed methodology comprises the study of the dynamic response for tracking the maximum power point and the power generated of the PV systems, and it was compared to the classic P&O technique under varying ambient conditions. We observed that the intelligent techniques outperformed the classic P&O method in tracking speed, tracking accuracy, and reducing oscillation around the maximum power point (MPP). The ANN technique was the better control algorithm in energy gain, managing to recover up to 9.9% power.

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