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# Horse Herd Optimization MPPT for PV System

## Introduction

We will delve into Horse Herd Optimization (HHO) MPPT for solar PV systems, focusing particularly on how it handles partial shading effects. We will base our discussion and implementation on a paper published in the Knowledge-Based Systems journal, titled "Horse Herd Optimization Algorithm." The link to this paper is provided in the description of our video.

## Implementation Overview

The HHO algorithm is implemented following a specific flowchart. Hereâ€™s a step-by-step breakdown:

1. Random Generation of Positions:Â We start by randomly generating the positions of the horses.

2. Fitness Function Calculation:Â Calculate the fitness function for each horse.

3. Age Calculation:Â Determine the ages of the horses, categorized as Alpha, Beta, Gamma, and Delta.

4. Velocity Function Application:Â Apply the velocity function to update the positions based on the horses' ages.

5. Stopping Criteria:Â Repeat the process until the stopping criteria are met.

This algorithm is used to extract the maximum power from the PV panel.

## MATLAB Code Explanation

The MATLAB code provided in the paper is essential for calculating the age of the horses and updating their velocities. Hereâ€™s a brief overview of the process:

• The age categorization is determined based on specific conditions:

• If CCOâ‰¤0.1Ã—NumberÂ ofÂ Horses\text{CCO} \leq 0.1 \times \text{Number of Horses}CCOâ‰¤0.1Ã—NumberÂ ofÂ Horses, it is classified as Alpha.

• If 0.1Ã—NumberÂ ofÂ Horses<CCOâ‰¤0.3Ã—NumberÂ ofÂ Horses0.1 \times \text{Number of Horses} < \text{CCO} \leq 0.3 \times \text{Number of Horses}0.1Ã—NumberÂ ofÂ Horses<CCOâ‰¤0.3Ã—NumberÂ ofÂ Horses, it is classified as Beta.

• If 0.3Ã—NumberÂ ofÂ Horses<CCOâ‰¤0.6Ã—NumberÂ ofÂ Horses0.3 \times \text{Number of Horses} < \text{CCO} \leq 0.6 \times \text{Number of Horses}0.3Ã—NumberÂ ofÂ Horses<CCOâ‰¤0.6Ã—NumberÂ ofÂ Horses, it is classified as Gamma.

• Otherwise, it is classified as Delta.

Using these age classifications, the velocity of each horse is updated, which in turn updates the duty cycle for the PV system.

## Simulink Model for HHO MPPT

The Simulink model tests the HHO algorithm for MPPT in a PV system. It receives two inputs: PV voltage (VPVV_{PV}VPVâ€‹) and PV current (IPVI_{PV}IPVâ€‹). The model calculates the PV power and applies the HHO algorithm to find the optimal duty cycle. This duty cycle controls a boost converter to maximize power extraction.

### Key Components:

1. PV Panel:Â Consisting of 60 cells, with 20 cells connected in series, and a total rating of 250 watts.

2. Boost Converter:Â Controlled by the duty cycle from the HHO algorithm to maximize power extraction.

3. Load:Â The load connected to the PV system to test power output.

## Simulation Results

Initially, we tested the model under uniform irradiation conditions, with all panels receiving 1000 W/mÂ². The maximum power output was around 249.8 watts. The HHO MPPT algorithm successfully extracted the maximum power from the PV panel under these conditions.

Next, we tested the model under partial shading conditions. The second panel's irradiation was set to 300 W/mÂ², while the others remained at 1000 W/mÂ². The theoretical maximum power was 160.9 watts, and the HHO MPPT algorithm extracted this maximum power efficiently.

We then tested another condition with irradiation values set to 1000 W/mÂ², 1000 W/mÂ², and 800 W/mÂ². The global peak power for this condition was 215.2 watts, and the HHO MPPT algorithm once again successfully extracted the maximum power.

## Conclusion

The Horse Herd Optimization MPPT algorithm proves effective in extracting maximum power from a PV panel, both under uniform and partial shading conditions. This robust algorithm ensures optimal performance, enhancing the efficiency of solar PV systems.