DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING

Solar photovoltaic (PV) technology is a critical low-carbon solution, but its performance is severely compromised by shading. This study addresses the persistent problem of partial shading, which causes disproportionate power losses and creates thermal stress risks like hot spots. This research aims to quantify the effect of shading on PV panel voltage, current, and power output under controlled laboratory conditions. The methodology employed an experimental approach using an SES TPS- 3720 Solar Energy Trainer. Experiments measured performance under 0% (baseline), 50% (partial), and 100% (full) shading. The study also evaluated the impact of shading material optical properties by testing opaque (wood), semi-opaque (paper), and translucent (plastic film) materials. Measurements were recorded across five irradiance levels using both LED lamp and DC motor loads.Key findings demonstrate a highly non-linear performance degradation. Partial shading covering 50% of the panel area resulted in a 65-70% power loss, far exceeding a proportional reduction.Full shading with opaque (wood) or semi-opaque (paper) materials caused a 100% power loss, eliminating all usable current. Translucent plastic film caused the least degradation (approx. 23% power loss). The results confirm that a material's optical transmittance, not its physical density, is the dominant factor determining shading severity. These findings validate established photovoltaic theory and highlight the critical importance of shadow avoidance in system design. The study reinforces the necessity of mitigation strategies such as bypass diodes and module-level power electronics (MLPE) in shade-prone installations.

This project addresses critical operational limitations in locally manufactured yam pounders by designing and implementing an adaptive Variable Frequency Drive (VFD)-based speed controller for a 1 HP single-phase yam pounder motor. Traditional yam pounding machines operate at fixed speeds, resulting in inconsistent pounding results, food quality degradation, limited user control, energy inefficiency, and accelerated machine wear. These shortcomings arise from the inability to accommodate variations in yam texture, moisture content, quantity, and regional preferences for different pounded yam consistencies. The primary objective of this work was to design, implement, and evaluate a speed control system that enables variable-speed operation while maintaining torque stability and energy efficiency. The system employs a microcontroller-based VFD architecture utilizing Arduino Due for Sinusoidal Pulse Width Modulation (SPWM) generation, IR2110 gate drivers for power stage control, and a full H-bridge inverter configuration with IGBTs. The control strategy implements Voltage-to-Frequency (V/f) control to maintain constant magnetic flux across varying operational frequencies, ensuring consistent torque output from 0 to 50 Hz. Comprehensive testing was conducted in progressive stages, beginning with low-voltage functional verification, followed by full-voltage no-load testing, and culminating in motor load testing with the 1 HP yam pounder motor. The system successfully demonstrated linear speed control from 0 to 2850 RPM with smooth torque response and minimal vibration. Key performance metrics included stable DC bus voltage at 325 V, accurate PWM carrier frequency of 10 kHz, maximum motor current draw of 4.2 A (within the 4.5 A rating), IGBT temperature of 42°C after 30 minutes of operation, and Total Harmonic Distortion (THD) of approximately 8.5% in the output voltage waveform.

Electrical power transmission lines are critical components of the power system, ensuring the delivery of electricity from generation to end-users. However, these systems are highly vulnerable to degradation caused by environmental conditions, mechanical stress, thermal effects, and aging infrastructure, which can lead to failures, outages, and safety risks. Traditional maintenance approaches—corrective, preventive, and predictive—have been widely used, but they are often limited in efficiency, cost-effectiveness, and reliability. In recent years, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies for predictive maintenance in electrical power transmission systems. This study explores the application of AI and ML techniques in enhancing predictive maintenance of transmission lines. It examines how advanced algorithms such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Support Vector Machines (SVMs), combined with data from Internet of Things (IoT) sensors, drones, and thermal imaging systems, can be used to detect early warning signs of faults such as overheating, insulation breakdown, and overcurrent conditions. The study also highlights the benefits of AI-driven predictive maintenance, including reduced downtime, lower operational costs, improved system reliability, enhanced asset lifespan, and greater energy efficiency. Despite these advantages, the study identifies challenges such as data quality issues, high implementation costs, and technical complexities in integrating AI systems into existing power infrastructure. The research concludes that AI and ML-based predictive maintenance represents a significant advancement over traditional maintenance approaches and is essential for modernizing electrical power transmission systems. It recommends increased investment in smart grid technologies and capacity building to support the adoption of intelligent maintenance systems for sustainable and reliable power delivery.

This research explores the application of Artificial Intelligence (AI) and Machine Learning (ML) for the predictive maintenance of transmission lines, specifically targeting fault detection, failure prediction, and maintenance optimization. Synthetic data was used to simulate parameters such as current, voltage, and temperature. Data preprocessing techniques, including cleaning and normalization, were performed. A supervised learning approach, the Random Forest Classifier, was applied using Python to mimic real-world fault scenarios. Model performance was evaluated using standard metrics: accuracy, precision, recall, and F1-score.The findings demonstrate that AI-based predictive maintenance has the potential to improve power system reliability and efficiency by reducing downtime and optimizing maintenance scheduling. The study also addresses key challenges, such as data availability and model generalization, proposing solutions like data augmentation and hybrid model design. Ultimately, this research provides a framework for developing scalable, data-driven predictive maintenance systems, advancing smart grid
technologies and sustainable power system management.

This research explores the application of Artificial Intelligence (AI) and Machine Learning (ML) for the predictive maintenance of transmission lines, specifically targeting fault detection, failure prediction, and maintenance optimization.
Synthetic data was used to simulate parameters such as current, voltage, and temperature. Data preprocessing techniques, including cleaning and normalization, were performed. A supervised learning approach, the Random Forest Classifier, was
applied using Python to mimic real-world fault scenarios. Model performance was evaluated using standard metrics: accuracy, precision, recall, and F1-score.The findings demonstrate that AI-based predictive maintenance has the potential to improve power system reliability and efficiency by reducing downtime and optimizing maintenance scheduling. The study also addresses key challenges, such as data availability and model generalization, proposing solutions like data augmentation and
hybrid model design. Ultimately, this research provides a framework for developing scalable, data-driven predictive maintenance systems, advancing smart grid technologies and sustainable power system management

The use of induction motor in various facet of engineering manufacturing and production sector to power various equipment have gained stability and thereby creating huge starting current which in turn contribute to the unbalance loading of network giving rise to high energy and economic loss. This research work therefore seeks to reduce the starting current of the connected single phase induction motor. A smooth and soft start is employed in a single phase induction motor to eliminate the surge incurrent and electromagnetic torque during starting. The surge in current an torque are eliminated using soft starter at the time of starting. The soft starter also eliminates the unwanted effect in electric cables and distribution network. This project work provides an in depth description of sentimental and smooth start to an induction motor. The smooth start of the motor is predicted by the firing angle of the TRIAC circuit. The firing angle is delayed during starting and the delay angle reduces as the motor picks up to speed. This proposed technique provided reduced voltage at the starting and the rated voltage when the motor is up to speed. By using soft starter, the performance and efficiency of the induction motor is improved and it also improves the load torque characteristics. This project consists of 6 anti- parallel SCR connected in each series with an induction motor to the main supply, wherein two to each phase. During starting the firing angle is heavily delayed by receiving a delayed triggering pulses. The supplied voltage is gradually increased and the torque also in same manner. By this process the inrush current is drastically reduced making the motor start smoothly. The induction motor of 0.56KW, frequency of 50Hz, maximum voltage 230V, receives little or no surge using the soft 6 starter device. The startup ramp is about 4s to 7s depending on the power of the induction motor. The firing angle is gradually decreased from 80˚ by interval of 20˚ until 0˚ max of the full half A.C voltage.

The chronic unreliability of Nigeria's national power grid necessitates a dependency on costly and environmentally damaging diesel generators, particularly for critical institutions like universities. The literature validates Hybrid Renewable Energy Systems (HRES), specifically the Photovoltaic (PV)-Diesel-Battery configuration, as a technically superior and sustainable alternative for off-grid power. However, a granular, site-specific reliability assessment for the unique and energy-intensive load profile of a Nigerian engineering faculty represents a significant gap in existing research. This study addresses this gap by providing a bespoke techno- economic analysis and reliability evaluation for a standalone hybrid power system for the Faculty of Engineering at the University of Benin. This research adopts a simulation-based methodology centered on the Hybrid Optimization Model for Multiple Energy Resources (HOMER) Pro software. The analysis is founded on a comprehensive on-site electrical load survey, which determined the faculty's detailed operational patterns and an annual energy demand of 737,686 kWh. This granular, real-world load profile, along with local solar irradiance and ambient temperature data for Benin City, was used to model, simulate, and optimize thousands of system configurations. The primary objective of the optimization was to identify the component sizing (PV array, battery bank, and diesel generator) that meets the faculty's load with the highest reliability at the lowest possible life-cycle cost. The simulation results identified an optimal system configuration consisting of a 525 kW PV array, a 198 kWh Battery Energy Storage System (BESS), and an 85kW diesel generator relegated to a backup role. This system achieves 100% reliability with zero unmet load, a 100% renewable energy fraction, and a highly competitive Levelized Cost of Energy (LCOE) of $0.0548/kWh. The analysis confirms that this configuration completely displaces the need for diesel fuel, thereby eliminating significant operational costs and preventing approximately 553 tonnes of CO2 emissions annually. The findings conclusively demonstrate that a properly sized PV-Battery hybrid system is a technically reliable, economically superior, and environmentally sustainable solution to the faculty's energy challenges.

This study details the design and comprehensive economic analysis of a standalone Photovoltaic (PV) inverter system tailored for selected load centre within the Faculty of Engineering. The primary aim was to devise an optimal, reliable, and sustainable energy solution capable of independently meeting the critical electrical load demands of the facility. This project directly addresses the necessity for a cleaner and cheaper power source in the face of continuous grid rising operational expenses, thereby proposing a resilient renewable energy alternative.
The methodology adhered to a structured process, beginning with load survey to precisely quantify the energy consumption patterns and peak demand. Following this, essential system components including the PV array, battery bank, and inverter were initially sized using fundamental mathematical equations. The
technical and economic optimization of the system was then conducted through dynamic simulation using the specialized HOMER Pro(version 64-3.11.2) software, which successfully identified a range of technically feasible solutions. The final critical step involved an in-depth economic analysis, comparing the lifecycle cost of the optimized PV system against the cost of energy supplied by the existing utility grid.
The simulation and economic assessment confirmed the viability and financial advantage of the designed PV inverter system. The Levelized Cost of Energy (LCOE) for the PV inverter system was calculated to be ₦143.47/kWh, demonstrating a significant cost benefit when compared to the utility grid supply's LCOE of
₦209.5/kWh. This result conclusively shows that the PV solution is cheaper and more sustainable over the 25-year project lifetime. With a favorable payback period calculated at 13.4 years, the study concludes that the proposed PV inverter system represents a robust, financially sound, and economically superior investment
for guaranteeing a continuous power supply to the Centre study details the design and comprehensive economic analysis of a standalone Photovoltaic (PV) inverter system tailored for a selected Centre within the Faculty of Engineering

Efficient power utilization is a key concern in modern electrical systems, especially in industries where large inductive loads cause a reduction in power factor and overall system efficiency In this part of the world, power factor correction has been accomplished through manually operated capacitor banks; however, manual systems are not flexible enough to react dynamically to changing load conditions. Therefore, this project focuses on the design and simulation of an Automatic Power Factor Correction (APFC) system using the Proteus software environment, aimed at improving the power factor of electrical systems operating under varying load conditions.
The project was done by deploying an Arduino Uno microcontroller-based control logic, supported by zero-crossing detectors to convert voltage and current waveforms into square signals for accurate phase difference and power factor calculation. The experimental setup was designed and simulated using Proteus 8 Professional software. The Proteus simulation replicates the real-time operation of the APFC system, enabling precise observation of voltage and current waveforms, zero-cross detection, and automatic capacitor switching. A resistive load and an inductive load were modelled to test the system’s capability to measure and correct the power factor dynamically. Simulation results showed a significant improvement in power factor after correction, confirming the effectiveness of the control strategy. Base load of 30mH; 60mH; 30mH and 60mH; 30mH and 90mH; 60mH and 120mH; 30mH, 60mH, 120mH and 90mH; had power factors of 0.87; 0.84; 0.78; 0.60; 0.58 respectively, but recorded tremendously improved power factors of 1.00; 0.91; 0.98; 0.95; 0.98 respectively after correction. As load increases, the system automatically activates additional capacitors to offset the rise in reactive power
demand, thereby enhancing voltage stability, reducing energy losses, and improving overall power efficiency.

Solar photovoltaic (PV) technology is a critical low-carbon solution, but its performance is severely compromised by shading. This study addresses the persistent problem of partial shading, which causes disproportionate power losses and creates thermal stress risks like hot spots.This research aims to quantify the effect of shading on PV panel voltage, current, and power output under controlled laboratory conditions.The methodology employed an experimental approach using an SES TPS-3720 Solar Energy Trainer. Experiments measured performance under 0% (baseline), 50% (partial), and 100% (full) shading.The study also evaluated the impact of shading material optical properties by testing opaque (wood), semi-opaque (paper), and translucent (plastic film) materials. Measurements were recorded across five irradiance levels using both LED lamp and DC motor loads.Key findings demonstrate a highly non-linear performance degradation. Partial shading covering 50% of the panel area resulted in a 65-70% power loss, far exceeding a proportional reduction.Full shading with opaque (wood) or semi-opaque (paper) materials caused a 100% power loss, eliminating all usable current. Translucent plastic film caused the least degradation (approx. 23% power loss).The results confirm that a material's optical transmittance, not its physical density, is the dominant factor determining shading severity.These findings validate established photovoltaic theory and highlight the critical importance of shadow avoidance in system design. The study reinforces the necessity of mitigation strategies such as bypass diodes and module-level power electronics (MLPE) in shade-prone installations.