SOLAR INVERTER

The rising demand for reliable electricity in regions with unstable grid power necessitates the adoption of renewable energy solutions. This project focuses on the design and construction of a 5 kVA, 48 V solar inverter system, capable of integrating solar, battery, and grid power sources for consistent and uninterrupted energy supply to domestic and small office loads. The system integrates solar photovoltaic panels, a battery bank, an MPPT charge controller, and a pure sine wave inverter. It also covers system design, component selection, fabrication, installation, and performance evaluation, with considerations for efficiency, safety, reliability, and cost effectiveness. Experimental testing involved load consumption analysis, continuity and polarity tests, inverter performance evaluation, battery charge–discharge assessment, and protective device verification. The inverter demonstrated a stable AC output voltage of 230 V ± 5 V, frequency of 50 Hz ± 1 Hz, system efficiency of approximately 89.5%, and a Total Harmonic Distortion (THD) of less than 3%. Protective features such as overload, short circuit, reverse polarity, and thermal protection were verified to function effectively. Cost analysis indicated that the local construction of the system is more affordable than imported equivalents, while environmental evaluation highlighted reduced carbon emissions and promotion of clean energy access. The project underscores the feasibility, scalability, and socio economic benefits of solar PV systems, offering a practical framework for renewable energy adoption in rural and semi urban areas.

This project involves the design and fabrication of a 1KVa solar inverter, which converts DC power from solar panels to AC power for household and industrial applications. The inverter is designed to operate at a high efficiency, with a minimum of 90% efficiency. The project also involves the development of a control system to regulate the output voltage and frequency of the inverter. Various efforts have been made to address this challenge, but existing alternatives, such as diesel generators, have proven to be inefficient, expensive, and environmentally unfriendly. They require frequent fueling, maintenance, and replacement of parts, while also contributing to pollution. The capacity of an inverter system depends on the specific application and the power requirements of the appliances being used. In this context, we are focusing on a domestic inverter system with a maximum capacity of 5KVA (4000 watts), designed to provide backup power during outages and serve as a primary source of energy when the national grid is unavailable.

Solar energy is a renewable and abundant resource that holds immense potential for meeting the world's growing energy demand while mitigating climate change. Accurate assessment of solar radiation patterns and availability is crucial for the efficient deployment of solar energy technologies and the optimization of renewable energy systems.This research project aims to conduct a comprehensive solar panel/ solar inverter resource assessment in the University Of Benin Ugbowo, Nigeria, to evaluate the solar energy potential around it. The study employs an approach which involved integrating satellite data analysis, gotten from the National Aeronautics and Space Administration (NASA) to quantify solar irradiance levels and characterize solar radiation . The findings of this research project will provide valuable insights into the periods of best insolation and also environmental factors influencing solar resource availability in the University Of Benin.Most domestic appliances such as personal computers, television sets and lighting systems among others, operation has largely been controlled by hydro-electric power supply [National Grid]. This is not much a reliable source of energy due to the persistent power failure from our only reliable Nation Grid in the country, which has been the result of low water level in the dam(s) during harmattan season, faulty underground cables and transformer failures. Nigeria's energy sector is characterized by a dominance of fossil fuels, which contributes to climate change, air pollution, and environmental degradation. The country's renewable energy resources, particularly solar energy, remain largely untapped. This project seeks to contribute to the development of Nigeria's solar energy sector by designing, installing, and testing a solar powered energy system with an 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%.