Comparative study of maximum power point tracking techniques for a stand-alone photovoltaic system

Mostafa abosennah, Mahmoud Abbas El-Dabah, Ahmed El-Biomey Mansour


Solar photovoltaic (PV) is one of the most important Renewable Energy Resources (RER). It has no fuel cost, no pollution, noiseless and low maintenance cost. PV systems have limited energy efficiency leading to increasing the number of PV units required for certain input power i.e. to higher initial cost. To overcome this problem, Maximum Power Point Tracking (MPPT) control methods are used.
This work introduces a comparative study of six MPPT classical and artificial intelligence (AI) techniques: Perturb and Observe (P&O), Modified Perturb and Observe (M-P&O), Incremental Conductance (INC), Fuzzy Logic Controller (FLC), Artificial Neural Network (ANN), and Adaptive Neoro-Fuzzy Inference System (ANFIS). Under the same climatic conditions, a comparison between these techniques in view of 9 criteria’s: efficiencies, tracking response, implementation cost, and others, will be performed.
Simulation results, obtained by using MATLAB/ SIMULINK program, show that the system without any control has the lowest efficiency. This efficiency is improved by using any MPPT technique with a small range of error. The Adaptive Neoro-Fuzzy Inference System (ANFIS) technique has the highest energy efficiency. The AI-techniques have higher tracking response over the conventional techniques. The ANN-technique has the highest response and the INC-technique has the lowest. P&O-technique has the highest oscillation, but this drawback is eliminated by using M-P&O-technique. The implementation cost for ANFIS is the highest due to the cost of sensors required, and P&O is the cheapest. The computing time required for the FLC-technique is the longest due to its software complexity, but it is for the INC is the shortest.


Renewable Energy Resources ; Photovoltaic (PV) ;MPPT techniques. ;Classical techniques ;Artificial Intelligence (AI)


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