MPPT Strategy of PV System Based on Adaptive Fuzzy Control

2014 ◽  
Vol 1008-1009 ◽  
pp. 63-67
Author(s):  
Jia Yuan ◽  
Yu Jiang Wang ◽  
Jing Hong Cui ◽  
Hui Li Zheng ◽  
Liu Bin ◽  
...  

In order to use photovoltaic cell effectively and improve its photoelectric conversion efficiency, the maximum power point of photovoltaic generation system should be tracked rapidly and stably [1]. Taking into account the solar PV systems are often affected by external factors,it is difficult to determine system parameters, and has a strong non-linear,so this paper,a adaptive fuzzy logic control technology for STP0950S-36 type of independent photovoltaic systems to a adaptive fuzzy controller design method, and using MATLAB/SIMULINK,fuzzy logic toolbox for simulation tools such as maximum power point control,adaptive fuzzy control simulation results of MPPT with fixed step method compared to fixed-step method was found to reach steady there is a certain state after the fluctuation,The results show that the method can quickly and correctly track change of MPP in different light intensity and the system has excellent stability performance.

Author(s):  
Ahmad Saudi Samosir ◽  
Herri Gusmedi ◽  
Sri Purwiyanti ◽  
Endah Komalasari

This paper presents modeling and simulation of maximum power point tracking (MPPT) used in solar PV power systems. The Fuzzy logic algorithm is used to minimize the error between the actual power and the estimated maximum power.  The simulation model was developed and tested to investigate the effectiveness of the proposed MPPT controller. MATLAB Simulink was employed for simulation studies. The proposed system was simulated and tested successfully on a photovoltaic solar panel model. The Fuzzy logic algorithm succesfully tracking the MPPs and performs precise control under rapidly changing atmospheric conditions. Simulation results indicate the feasibility and improved functionality of the system.


2021 ◽  
pp. 1-10
Author(s):  
Imran Pervez ◽  
Adil Sarwar ◽  
Afroz Alam ◽  
Mohammad ◽  
Ripon K. Chakrabortty ◽  
...  

Due to its clean and abundant availability, solar energy is popular as a source from which to generate electricity. Solar photovoltaic (PV) technology converts sunlight incident on the solar PV panel or array directly into non-linear DC electricity. However, the non-linear nature of the solar panels’ power needs to be tracked for its efficient utilization. The problem of non-linearity becomes more prominent when the solar PV array is shaded, even leading to high power losses and concentrated heating in some areas (hotspot condition) of the PV array. Bypass diodes used to eliminate the shading effect cause multiple peaks of power on the power versus voltage (P-V) curve and make the tracking problem quite complex. Conventional algorithms to track the optimal power point cannot search the complete P-V curve and often become trapped in local optima. More recently, metaheuristic algorithms have been employed for maximum power point tracking. Being stochastic, these algorithms explore the complete search area, thereby eliminating any chance of becoming trapped stuck in local optima. This paper proposes a hybridized version of two metaheuristic algorithms, Radial Movement Optimization and teaching-learning based optimization (RMOTLBO). The algorithm has been discussed in detail and applied to multiple shading patterns in a solar PV generation system. It successfully tracks the maximum power point (MPP) in a lesser amount of time and lesser fluctuations.


Author(s):  
C. Pavithra ◽  
Pooja Singh ◽  
Venkatesa Prabhu Sundramurthy ◽  
T.S. Karthik ◽  
P.R. Karthikeyan ◽  
...  

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