## 3 Detailed Analysis of Maximum Power Point Tracking (MPPT) Methods
### ANN (Artificial Neural Network)-Based MPPT Method
#### 3.1.2 Design Details  
The design of the ANN-based MPPT algorithm is structured to effectively model the nonlinear and dynamic behavior of PV systems. The following subsections detail the architectural and operational aspects of the neural network used for MPPT.  
* Inputs  
The input layer of the ANN is designed to receive real-time and relevant environmental and system parameters that directly influence the PV system's output. These inputs include the following:  
Solar irradiance: This parameter represents the intensity of sunlight on the surface of the PV panels, typically measured in W/m\({}^{2}\). It is a primary factor affecting the power generation capability of the PV system.  
Temperature: The ambient temperature, usually measured in degrees Celsius, impacts the efficiency and output characteristics of PV modules. Higher temperatures generally reduce the voltage output of PV cells.  
Load power: This is the instantaneous power demand from the load connected to the PV system. Including load power as an input allows the ANN to consider the system's operational context when predicting the MPP.  
All input data are normalized before being fed into the network to ensure stable and efficient training.
* Hidden Layers  
The hidden layer is the core computational component of the ANN, which is responsible for learning the complex, nonlinear relationships between the input variables and the MPP.  
Number of layers: The network utilizes a single hidden layer, which is sufficient for capturing the essential nonlinearities in most MPPT applications.  
Number of neurons: The hidden layer consists of 10 neurons. This number is chosen as a balance between model complexity and computational efficiency, ensuring that the network can learn intricate patterns without overfitting.  
Activation function: Each neuron in the hidden layer uses a sigmoid activation function. The sigmoid function introduces nonlinearity and allows the network to model complex relationships between inputs and outputs.
* Training Process  
The effectiveness of the ANN-based MPPT algorithm depends heavily on the quality of the training process.  
Training data: The network is trained using a comprehensive dataset that includes historical records of solar irradiance, temperature, and load power, along with the corresponding MPPs. The dataset should cover a wide range of environmental conditions to ensure robust generalization.  
Error function: The Mean Squared Error (MSE) is used as the loss function during training, defined as Equation (3):  
\[MSE=\frac{1}{n}{\sum_{i=1}^{n}}{\left({\rm{yi}}-yi^{i}\right)^{2}} \tag{3}\]  
where \(yi\) is the actual value and \(yi^{i}\) is the predicted value.  
Optimization: The network's weights are updated using the backpropagation algorithm, which computes the gradient of the error function with respect to each weight and adjusts them to minimize the overall error. An appropriate learning rate is selected to ensure convergence without overshooting.
* Implementation Considerations  
Data preprocessing: input data normalization and, if necessary, feature scaling are performed to improve training stability and convergence speed.  
Overfitting prevention: techniques such as early stopping or regularization may be employed to prevent overfitting, especially if the training dataset is limited.  
Deployment: once trained, the ANN model is embedded into the MPPT controller, where it processes real-time sensor data and outputs the optimal operating point for the PV system.  
This detailed design ensures that the ANN-based MPPT algorithm can adapt to varying environmental conditions and provide accurate, real-time predictions for maximum power extraction from PV systems.