M.A. Thesis:

finding the maximum point tracking photovoltaic panels (pv) using the neural network and control method p&o

In order to make the photovoltaic panels (PV) fruitful, we need some control methods like tracing the maximum point (MPPT), specially the places in which the average of sun radiation or thee temperature is low. According to the power-voltage characteristic, we are supposed to find the maximum power in which the system power is in the highest rate, that is named MPP. MPP tracing is done with different methods which include classic types that based on direct and indirect tracing. The direct tracing is run based on the power-voltage curve method and the indirect tracing is run like optimization algorithms. In this field, the final growth of the researches leads to analyze the results quickly and as much as possible. It is often difficult for us to compare the MMPT algorithms fairly and meaningfully, therefore it is necessary for us to have a systematic path. Among these MPPT control algorithms, the P&O method has one of the most extensive usage, because of its ease of implementation. However, the major drawback of this algorithm is inaccuracy to the reason of oscillations around MPP. In the other hand, MPPT control which is run using neural networks shows that it is a very efficient solution with a sufficient accuracy. However, this approach is complex. In this chapter, first, we are going to present the P&O method, and then we will suggest the original optimization method of P&O control using a neural network algorithm. This method will significantly reduce our compensation costs requires for educating, and then it will guarantee a compromise between accuracy and complexity. This algorithm was applied for different models of solar panels that are confirmed as a simulation in MATLAB/Simulink

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