Artificial Neural Network for MPPT controller

$$ Input = I_{pv} + V_{pv} $$

$$ Output = I_{mppt} + V_{mppt} $$

After that the duty cycle (D) will be calculated for mppt tracking as P&O technique utilise D to define the location where maximum power be found then go back and forth to keep the algorithm running. The idea is adding (ANN) on P&O to enhance the algorithm more accurate tracking.

Every neural network consists of layers of nodes, or artificial neurons—an input layer, one or more hidden layers, and an output layer. Each node connects to others, and has its own associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network. [[CrossRef](https://www.ibm.com/topics/neural-networks#:~:text=Every neural network consists of,own associated weight and threshold.)]

Neural networks rely on training data to learn and improve their accuracy over time. Once they are fine-tuned for accuracy, they are powerful tools in computer science and artificial Intelligent , allowing us to classify and cluster data at a high velocity.

Comparing different designs:

Fig x: MPPT neural network structure diagram with 2 inputs, 7 layers (Temperature and Irradiation)

Fig x: Chosen variable for project inputs (PV current and voltage), 6 layers.

Fig x: Simulink reference for designing .