Dual-Mode Control MPPT Algorithm Based on Improved Genetic Algorithm and Perturbation and Observation Method

2012 ◽  
Vol 614-615 ◽  
pp. 1367-1371
Author(s):  
Yu Hong Zhao ◽  
Ze Guang Su ◽  
Zu Hua Xu
Keyword(s):  
Genetic Algorithm ◽  
Photovoltaic Cells ◽  
Maximum Power ◽  
Improved Genetic Algorithm ◽  
Tracking Accuracy ◽  
Dual Mode ◽  
Maximum Power Point ◽  
Mode Control ◽  
Power Point ◽  
Observation Method

To improve the tracking accuracy of the maximum power point of photovoltaic cells can improve the operational efficiency of photovoltaic power generation system and reduce the cost of photovoltaic electricity. Because the common perturbation and observation (P&O) runs at a low speed, this paper proposes a new dual-mode control MPPT algorithm based on improved genetic algorithm and perturbation & observation method. Then this method was used in the experiment. From the experimental results can be seen, the algorithm overcomes the shortcomings of the traditional perturbation and observation method can quickly and accurately tracking the maximum power point of photovoltaic cells output, you can improve system output power.

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.

An Improved Maximum Power Point Tracking Method

2013 ◽  
Vol 433-435 ◽  
pp. 1250-1253
Author(s):  
Xue Cheng Zhao ◽  
Yi Rui Zhao ◽  
Yu Hong Zhao
Keyword(s):  
Genetic Algorithm ◽  
Power Output ◽  
Maximum Power ◽  
Duty Ratio ◽  
Improved Genetic Algorithm ◽  
Maximum Power Point ◽  
Tracking Method ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

A duty ratio perturbation based on improved genetic algorithm is proposed in this paper. It can overcome some shortcomings of traditional MPPT method, and can perfectly achieve a real-time adjustment for maximum power output. A simulation model is established on Matlab, feasibility and validity of the method is verified.

scholarly journals Modeling and Simulation of an Intelligent Photovoltaic Controller Based on Variable Step Algorithm of Versoria

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Bo Sun ◽  
Yongquan You ◽  
Zhiyong Zhang ◽  
Chao Li
Keyword(s):  
Control Algorithm ◽  
Photovoltaic Cells ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Step Algorithm ◽  
Output Characteristics ◽  
Power Point ◽  
Fuzzy Control Algorithm

As a green and renewable energy source, photovoltaic power is of great significance for the sustainable development of energy and has been increasingly exploited. The photovoltaic controller is the key component of a photovoltaic power generation system, and its central technology is the maximum power point tracking technology. In this paper, a mathematical model of photovoltaic cells is firstly established, the output characteristics of photovoltaic cells are analyzed, the main factors that affect the output efficiency of photovoltaic cells are obtained, and it is proved that the most important factor that affects the output power is the light intensity. Therefore, in the design, the maximum power point of the photovoltaic cell is tracked by the control algorithm and can maximize the use of photovoltaic output power fast charging. The key to the design of a photovoltaic controller is the design of control algorithm. So, an improved fuzzy control algorithm is proposed to overcome the shortcomings of the traditional maximum power point tracking (MPPT) algorithm. The algorithm can consider tracking both speed and convergence, but the algorithm requires high input and output fuzzy domain parameters, and although the tracking speed is fast, the stability of convergence is poor. For the limitation of fuzzy control algorithm, considering the property of the Versoria function, an MPPT design method for an intelligent controller based on the Versoria variable step algorithm is further proposed. According to the output characteristics of photovoltaic cells, three parameters, α, β, and γ, are set to solve the tracking speed and tracking stability. In order to reduce the static error, a genetic factor is proposed to sum up the historical error to effectively improve the tracking stability. The simulation results show that the algorithm can track the maximum power point quickly and has good tracking speed and stability. This algorithm can be used in engineering practice effectively.

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