This video explains the MATLAB implementation of hybrid neural network incremental conductance mppt for PV battery grid system. The simulation results are explained for hybrid neural network incremental conductance mppt with varying irradiance conditions.

## Hybrid Neural Network Incremental Conductance MPPT in MATLAB

## Introduction

The Maximum Power Point Tracking (MPPT) is an essential part of any renewable energy system to ensure maximum efficiency. One of the most popular and effective methods for MPPT is the Incremental Conductance (IC) algorithm. However, the IC algorithm requires accurate measurements of the derivative of the power with respect to voltage, which can be challenging in noisy environments. In recent years, Hybrid Neural Networks (HNN) have been proposed as a solution to this problem. In this article, we will discuss how to implement the HNN IC algorithm in MATLAB.

## The Incremental Conductance Algorithm

The Incremental Conductance algorithm is a popular and widely used MPPT technique. It works by comparing the instantaneous conductance of the solar panel to a reference conductance. When the two conductances are equal, the system has reached the maximum power point. The algorithm then adjusts the duty cycle of the DC-DC converter to maintain the maximum power point.

The Incremental Conductance algorithm requires accurate measurements of the derivative of the power with respect to voltage. This can be challenging in noisy environments, and the algorithm may oscillate around the maximum power point. To overcome this problem, a Hybrid Neural Network can be used.

## Hybrid Neural Network

A Hybrid Neural Network combines the advantages of both artificial neural networks and traditional control techniques. The neural network can learn and adapt to the system's behavior, while the traditional control technique can ensure stability and robustness.

The Hybrid Neural Network Incremental Conductance algorithm uses a neural network to estimate the derivative of the power with respect to voltage. The neural network takes as input the current and voltage of the solar panel and outputs the estimated derivative. The neural network is trained using the backpropagation algorithm.

## Implementing the Hybrid Neural Network Incremental Conductance Algorithm in MATLAB

To implement the Hybrid Neural Network Incremental Conductance algorithm in MATLAB, we first need to design and train the neural network. We can use the Neural Network Toolbox in MATLAB to design and train the neural network.

Once the neural network is designed and trained, we can implement the Hybrid Neural Network Incremental Conductance algorithm as follows:

Read the current and voltage from the solar panel.

Feed the current and voltage into the neural network to estimate the derivative of the power with respect to voltage.

Calculate the conductance of the solar panel using the estimated derivative.

Compare the conductance to the reference conductance to determine if the system has reached the maximum power point.

Adjust the duty cycle of the DC-DC converter to maintain the maximum power point.

We can use the Simulink toolbox in MATLAB to simulate the system and evaluate its performance.

## Advantages of the Hybrid Neural Network Incremental Conductance Algorithm

The Hybrid Neural Network Incremental Conductance algorithm has several advantages over traditional Incremental Conductance algorithm:

More accurate estimation of the derivative of the power with respect to voltage, even in noisy environments.

Faster tracking of the maximum power point.

Improved stability and robustness.

Can adapt to changes in the solar panel characteristics.

## Conclusion

The Hybrid Neural Network Incremental Conductance algorithm is an effective and robust method for Maximum Power Point Tracking in renewable energy systems. In this article, we have discussed how to implement the algorithm in MATLAB using a Hybrid Neural Network. The Hybrid Neural Network Incremental Conductance algorithm has several advantages over traditional Incremental Conductance algorithm, including improved accuracy, faster tracking, and improved stability.

## FAQs

### What is Maximum Power Point Tracking (MPPT)?

MPPT is a method used in renewable energy systems to ensure maximum efficiency by tracking the maximum power point of the solar panel.

### What is the Incremental Conductance Algorithm?

The Incremental Conductance algorithm is a widely used MPPT technique that works by comparing the instantaneous conductance of the solar panel to a reference conductance.

### What is a Hybrid Neural Network?

A Hybrid Neural Network combines the advantages of both artificial neural networks and traditional control techniques. The neural network can learn and adapt to the system's behavior, while the traditional control technique can ensure stability and robustness.

### Can the Hybrid Neural Network Incremental Conductance Algorithm adapt to changes in the solar panel characteristics?

Yes, the Hybrid Neural Network Incremental Conductance algorithm can adapt to changes in the solar panel characteristics, making it a more flexible and robust solution for MPPT.

### What are the advantages of using the Hybrid Neural Network Incremental Conductance Algorithm?

The Hybrid Neural Network Incremental Conductance algorithm offers several advantages over traditional Incremental Conductance algorithms, including improved accuracy, faster tracking, and improved stability.

In conclusion, the Hybrid Neural Network Incremental Conductance algorithm is a powerful and robust method for Maximum Power Point Tracking in renewable energy systems. By using a neural network to estimate the derivative of the power with respect to voltage, the algorithm can provide more accurate and reliable results, even in noisy environments. Implementing this algorithm in MATLAB is relatively straightforward, and the Simulink toolbox can be used to simulate and evaluate the system's performance. Overall, the Hybrid Neural Network Incremental Conductance algorithm is a significant advancement in the field of renewable energy and has the potential to improve the efficiency and reliability of renewable energy systems.

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