# Reproducibility Package for Hybrid Standalone Microgrid Control Study

This repository contains the MATLAB implementation, datasets, and scripts used to reproduce the simulation results reported in the manuscript:

"A Novel Intelligent Control using RBFNN-Class Topper Optimization for Stability Enhancement in Hybrid Standalone Microgrid"

## Software Requirement

MATLAB R2020 or later.

## Folder Contents

Datasets:

* solar_irradiance_profile.csv → Solar irradiance variation profile used in simulation
* wind_speed_profile.csv → Wind speed variation profile used in simulation
* load_profile.csv → Dynamic load variation (2.5–10 kW)
* microgrid_parameters.csv → System parameters used in the hybrid microgrid model

MATLAB Scripts:

* DCVoltResponse_5.m → DC-link voltage response under load variation
* Source_Power_Sharing_6.m → Power sharing among PV, wind, and battery
* InvVolt_LoadCurrent7_8.m → Inverter voltage and load current waveforms
* FFT_9.m → Harmonic spectrum analysis of load current
* Comparision_Table3.m → Performance comparison of controllers
* Stabilityplot.m → Lyapunov stability analysis plot
* DC_link_volt_Comp_extra.m → Additional DC-link analysis
* BDC11_12_13.m → Battery converter behaviour

## How to Run the Simulation

1. Place all CSV dataset files in the same folder as the MATLAB scripts.
2. Open MATLAB.
3. Set the working directory to the project folder.
4. Run the scripts individually to reproduce the results:

* DCVoltResponse_5
* Source_Power_Sharing_6
* InvVolt_LoadCurrent7_8
* FFT_9
* Comparision_Table3
* Stabilityplot

Each script reproduces the corresponding figures and performance metrics reported in the manuscript.

## Dataset Description

Solar Irradiance Profile:
1000 → 600 → 900 W/m² variation used to test renewable intermittency.

Wind Speed Profile:
12 → 8 → 10 m/s variation used to evaluate controller robustness.

Load Profile:
Dynamic load variation from 2.5 kW to 10 kW (25–100% rated load).

## Purpose

This package is provided to ensure reproducibility of the simulation results and allow researchers to validate the proposed RBFNN–CTOA control strategy for hybrid standalone microgrids.
