Typical diagram of the MPPT control in a Photovoltaic (PV) System [2].

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... tracking the Maximum Power Point in the PV system. The MPPT effectiveness is depending on both the MPPT control algorithm and the MPPT circuit. The MPPT control algorithm is frequently applied in the DC-DC converter, which is generally employed as the MPPT circuit. Typical diagram of the MPPT connection in a Photovoltaic system is illustrated in Fig. 1. One of the most popular MPPT algorithms is the Perturb and Observe (P & O) method. Though, the drawbacks of this method are oscillation and convergence problem occurred at certain points during the tracking. To improve the P & O algorithm performance, Pongsakor Takun et al. [2] applied the Fuzzy Logic in their MPPT algorithm. In this ...

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... networks behavior. It is widely used in modeling complex relationships between inputs and outputs in nonlinear systems. ANN can be defined as parallel distributed information processing structure consisting of inputs, and at least one hidden layer and one output layer. These layers have processing elements called neurons interconnected together (Fig. ...

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... in this paper has two inputs which are temperature and solar irradiance. This ANN has two layers (one hidden and one output layers). The hidden layer has ten neurons having tansigmoid activation function and the output layer has just one neuron having purelin activation function. The output of this neuron is the current at maximum power point. In Fig. 12 is illustrated the ANN used in this work. ...

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... proposed MPPT control consists of two main parts, ANN and current control, as depicted in Fig. 11. In [4] was detailed the DC-DC Boost converter used in this work (Fig. ...

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... proposed MPPT control consists of two main parts, ANN and current control, as depicted in Fig. 11. In [4] was detailed the DC-DC Boost converter used in this work (Fig. ...

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... training and testing the used ANN (Fig. 12), we have employed a database having 104 couples of input/Target. One input is a couple of two temperature value and insolation value. The target is the corresponding value of ...

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... TABLE II, are listed a part of the database used for the training and testing the ANN used in this work (Fig. 10). (FIG. 12) V. RESULTS AND DISCUSSION In this section we will make a comparative study between the proposed MPPT control algorithm using the ANN presented in Fig. 12 one using ANN (Fig.12) both in terms of efficiency and fast response time. No oscillations around the maximum power point and easy implementation are the main advantages of ...

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... TABLE II, are listed a part of the database used for the training and testing the ANN used in this work (Fig. 10). (FIG. 12) V. RESULTS AND DISCUSSION In this section we will make a comparative study between the proposed MPPT control algorithm using the ANN presented in Fig. 12 one using ANN (Fig.12) both in terms of efficiency and fast response time. No oscillations around the maximum power point and easy implementation are the main advantages of this ...

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... TABLE II, are listed a part of the database used for the training and testing the ANN used in this work (Fig. 10). (FIG. 12) V. RESULTS AND DISCUSSION In this section we will make a comparative study between the proposed MPPT control algorithm using the ANN presented in Fig. 12 one using ANN (Fig.12) both in terms of efficiency and fast response time. No oscillations around the maximum power point and easy implementation are the main advantages of this controller. The latter has a convergence time better than that of the conventional Perturb and Observation (P&O) ...

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... 12) V. RESULTS AND DISCUSSION In this section we will make a comparative study between the proposed MPPT control algorithm using the ANN presented in Fig. 12 one using ANN (Fig.12) both in terms of efficiency and fast response time. ...