SOLAR INVERTER

This project presents the optimization of solar inverter efficiency using machine learning algorithms to improve power generation accuracy and system reliability under varying environmental conditions. Traditional solar inverter systems and Maximum Power Point Tracking (MPPT) methods often experience limitations in adapting to fluctuations in solar irradiance, temperature, and shading conditions, leading to reduced efficiency and energy loss. To address these challenges, this study developed and evaluated machine learning models capable of predicting and optimizing inverter performance in real time. Environmental and operational data including irradiance, temperature, day, hour, and inverter performance metrics were collected from the NASA and NSRDB datasets for the University of Benin region. Data preprocessing techniques such as normalization, interpolation, and feature engineering were applied before model training. Three machine learning models — Random Forest (RF), Gradient Boosting Machine (GBM), and Artificial Neural Network (ANN) — were implemented and evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). Results showed that the ANN model outperformed the other models with an MAE of 0.019, RMSE of 0.029, and R² value of 0.962. The optimized system achieved an efficiency improvement of 8.3% compared to conventional MPPT methods. The study further demonstrated the capability of machine learning algorithms to adapt to changing environmental conditions and improve solar inverter performance. The developed model was deployed using Django REST Framework for real-time prediction and monitoring. This research confirms that machine learning-based optimization can significantly enhance solar inverter efficiency, reduce energy losses, and contribute to sustainable and intelligent renewable energy systems.

The growing global demand for renewable energy has driven significant advancements in solar energy technology, particularly in photovoltaic (PV) systems and inverters, which convert solargenerated DC into usable AC. Despite progress, traditional inverters face challenges such as inefficiency, high harmonic distortion, and limited adaptability to dynamic environmental conditions.This project aims to design a microcontroller-based solar inverter that integrates
advanced control algorithms like Maximum Power Point Tracking (MPPT) and Pulse-Width Modulation (PWM) to enhance efficiency, reliability, and adaptability. By leveraging modern microcontroller technology, the project seeks to improve energy conversion, reduce costs, and address the limitations of conventional designs, contributing to the broader adoption of solar energy systems. The process begins with modeling the photovoltaic (PV) array using Simulink’s Simscape Electrical library, incorporating real-world parameters such as irradiance and temperature to simulate I-V and P-V curves. The MPPT algorithm, specifically the Perturb and Observe (P&O) method, is implemented to optimize power extraction under varying conditions. PWM is generated using a PID controller to regulate the DC-DC boost converter, which steps up the PV voltage. An H-Bridge inverter, controlled by Sinusoidal PWM (SPWM), converts the boosted DC into a clean AC waveform. The complete system integrates the PV array, MPPT, boost converter, and inverter, with simulations conducted to validate performance under diverse environmental and load conditions. This project successfully designed and simulated a microcontroller-based solar inverter system. The PV array, modeled under varying irradiance and temperature conditions, consistently generated around 5300W, operating near its maximum power point. The boost converter efficiently stepped up the PV voltage to 275.1V with over 90% efficiency, while the H-bridge inverter produced a clean 220V AC output with minimal harmonic distortion. System integration demonstrated robust performance under diverse environmental and load conditions, achieving an overall efficiency exceeding 90%.