Project Title: Artificial Intelligence-based Photovoltaic System Fault Detection and Localization.
Abstract:
This project focuses on using artificial intelligence (AI) to enhance the reliability and efficiency of photovoltaic (PV) systems by detecting, classifying and localizing faults. With the increasing adoption of solar energy systems globally, ensuring their continuous and optimal operation has become crucial. The main challenge addressed is the timely and accurate detection and the classification and localization of faults that can increase the efficiency of the PV system and decrease the maintenance costs. This project aims to develop an AI model capable of detecting and classifying and localizing different types of faults in PV arrays, ensuring a stable power output.
Students Names: Aseel S. Almalki, Rabab N. Pakari
Supervisor: Dr. S. M. Muyeen
Project Title: Design And Implementation Of D-Statcom To Compensate The Harmonics Contributed By The EV Charger.
Abstract:
Electric vehicle (EV) is found to be the best solution towards the achievement of a green earth. The electric vehicles need charging stations. Due to the electronic converter based interface, an EV charging station acts as a nonlinear load. These power electronic converters introduce nonlinearities in drawing the grid current and cause harmonic distortion due to the switching action. Thus, the power quality of the electrical system becomes at risk. The distributed static synchronous compensator (D-STATCOM) is found to be an effective solution for minimizing these harmonics. This report describes the design of a 1KVA D-STATCOM controlled with appropriate controllers to improve harmonic profile of the EV charging station. A single level inverter scheme for D-STATCOM is targeted variable voltage levels and reduced harmonic distortion. As the number of levels in a Sigle level inverter increases, the output voltage waveforms become nearly sinusoidal and drastically reduces the harmonic content in the voltage and current waveforms. Furthermore, the voltage burden on each switch reduces while increasing the number of levels. To obtain the total harmonic distortion in the grid current within the allowable limit of 6%, the optimum number of levels will of single level inverter will be found to obtain the desired 1KVA D-STATCOM. The performance of the proposed single level inverter based 1KVA D-STATCOM will be evaluated through MATLAB/Simulink simulations.
Students Names: Ganna H. Hassan, Noof A. Al-Sailani, Rodhah M. Al-Maadeed
Supervisor: Dr. Atif Iqbal
Project Title: Design of Clear Glass Solar PV based Green House for Qatar.
Abstract:
This project focuses on the design and optimization of a solar photovoltaic (PV) greenhouse system, focusing on two key subsystems: energy generation and storage, and optimization. Subsystem 1 aims to maximize energy output by carefully designing the layout of clear glass solar PV panels, utilizing advanced Maximum Power Point Tracking (MPPT) technology, and incorporating energy storage elements such as batteries and heat pumps. This subsystem ensures a reliable power supply under Qatar’s challenging climate conditions, operating in both off-grid and on-grid modes. Subsystem 2 focuses on the optimization of the system’s energy management. It involves determining the optimal sizing of battery storage based on the greenhouse’s energy demand and solar generation data. Using heuristic optimization techniques, the system seeks to minimize grid dependency and maximize energy efficiency. The preliminary results suggest that an effective battery size of approximately 15.3 kWh is required to meet the energy demand during periods of low solar generation. This project provides insights into the potential of integrating renewable energy systems with optimization algorithms to achieve energy efficiency and sustainability in greenhouse operations.
Students Names: Fathima saarah Saffan Mohamed, Shorouq A. Abdulla
Supervisor: Dr. S. M. Muyeen
Project Title: Monitoring Qatar’s Urban Biodiversity Using Acoustic Signal Analysis.
Abstract:
Globally, environmental protection and biodiversity management are critical concerns, particularly in places like Qatar, which hosts over 350 different bird species. This project develops an innovative bird sound identification system, leveraging advanced signal processing and Artificial Intelligence techniques. By applying signal processing methods, raw and noisy audio data are enhanced producing clean, high-quality signals suitable for training Machine Learning and Deep Learning models. For each of the algorithms, feature extraction and parameter fine tuning is done to ensure the highest level of accuracy. For all Machine Learning Algorithms, accuracy scores are compared using raw as well as processed data. Accuracy is also compared over different number of classes. This approach aims to enhance our understanding of Qatar’s avian biodiversity, offering detailed insights into species composition, relative abundance, spatial distribution, and key biodiversity indicators. These findings can inform and guide effective conservation strategies, contributing to global efforts to preserve and protect diverse ecosystems.
Students Names: Aqsaa Adil, Fatima K. Alsulaiti, Fatima S. Ahmed
Supervisor: Dr. Muhammad Salman Khan
Project Title: Design and Implementation of flyback Converter Using a Planner Transformer used in Switch Mode Power Supply (SMPS).
Abstract:
This project aims to create and construct a high-efficiency flyback converter and a planar transformer for use in Switched Mode Power Supplies (SMPS). The aim is to design a compact
and efficient power conversion system that can provide a stable 15V DC output from an input voltage range of 60-75V and has a power rating of 100 Watts. The project addresses the
shortcomings of standard transformers, such as their large size and inefficiency, by using a planar transformer that improves thermal performance, reduces electromagnetic interference
(EMI), and boosts power density the process involves designing and implementing the flyback converter, such as the planar transformer and a closed-loop control system the implementation involves testing of performance parameters such as voltage control, efficiency, and thermal behavior the findings show that the system meets its design objectives, producing steady output under variable load and input circumstances. The work is important for industries like anaircraft, defense, and telecommunications, where small and lightweight power supply are required.
Students Names: Ghalya A. Al-Buhendi, Hend S. Alnaimi, Shahad G. Al-Marzooqi
Supervisor: Dr. Atif Iqbal
Project Title: Computer Vision-Based Smart Home Care System for Alzheimer’s Patients.
Abstract:
This project introduces an innovative vision-based smart home care system to be designed to enhance the safety and well-being of Alzheimer’s patients by providing real-time notifications to caregivers, helping to prevent accidents like falls and wandering. The system integrates live tracking technology, computer vision-based fall detection, and an IoT-enabled caregiver interface to provide continuous monitoring and support. This report reviews existing solutions for Alzheimer’s patients, examines current technologies, outlines the design constraints and standards for developing a personalized smart home care system, and includes the initial design for the project. By addressing the unique needs of individuals with Alzheimer’s disease, this project aims to improve their quality of life.
Students Names: Aisha A. Al-Mass, Albatool E. Al-Mahmoudi, Fatima M. Albahar
Supervisor: Dr. Faycal Bensaali
Project Title: Next Generation Perovskite Solar Energy Technology Development for Qatar Environment.
Abstract:
Perovskite Solar Cells (PSCs) are a promising alternative to traditional silicon-based solar cells due to their high efficiency, low production costs, and flexible design. However, challenges like poor light absorption, charge recombination, stability issues, and scalability limit their widespread use. This project addresses these challenges by improving three key parts of the solar cell: the active layer, the front contact-electron transport layer, and the back contact layer. The active layer is optimized to increase light absorption, improve material quality, and reduce energy loss due to recombination, achieving an efficiency of 21.5%. The front contact and electron transport layers (FTO/ITO-TiO₂) are enhanced to reduce optical losses, improve conductivity, and ensure smooth charge transfer, leading to an efficiency of 21.89%. For the back contact layer, the focus is on selecting materials that balance cost, conductivity, and stability, while also reducing optical losses and improving long-term performance, achieving the highest efficiency of 21.99%. By using simulations and experimental testing, the project evaluates the combined performance of these layers to achieve a power conversion efficiency (PCE) of over 15%, surpassing the original goal. The results show improvements in current density, efficiency, and long-term stability, even in challenging conditions like high temperature and humidity. Future work will focus on testing the PSC structure under real-world conditions, particularly in Qatar’s climate, to ensure reliability under high heat and humidity. Additional efforts will explore new materials to reduce the use of lead while maintaining efficiency and stability. To improve performance further, enhanced interlayer connections will be developed to minimize energy loss, and advanced manufacturing methods, like roll-to-roll printing, will be explored for scalable production. The project will also consider integrating PSCs with batteries to enable broader applications. Finally, long-term outdoor testing will be conducted to assess stability, and the environmental impact of the materials will be studied to ensure safe, sustainable, and eco-friendly solar cell production. This comprehensive approach advances PSC technology, making it more stable, efficient, and ready for large-scale production as a clean and affordable energy source.
Students Names: Ghalya N. Al-mannai, Kaltham A. Al-Jassim, Shooq A. Al-Hadad
Supervisor: Dr. Farid Touati
Project Title: Simultaneous Localization and Mapping Mobile Robot.
Abstract:
This project details the design and implementation of a robotic system for Simultaneous Localization and Mapping (SLAM) in structured indoor environments, such as office corridors. The system consists of two main subsystems: the sensor subsystem, which includes a camera for number recognition to determine the location by recognizing office numbers, a LiDAR for mapping and obstacle detection, and an ultrasonic sensor for close-range and backup obstacle detection; and the drive subsystem, which comprises motors, wheels, and a chassis controlled by a Raspberry Pi to enable movement and navigation. Motivated by the need for efficient and reliable navigation in confined spaces, this project addresses limitations in previous approaches, such as computational inefficiency and impracticality. By integrating cost-effective sensors and designing a robust control framework, the design ensures compatibility with functional, technical, and economic constraints. The methodology focused on individual testing of the camera for digit recognition and the LiDAR for mapping and obstacle detection, with plans to incorporate the ultrasonic sensor and integrate all subsystems in future work. The results demonstrate successful operation of the camera and LiDAR subsystems independently. The drive subsystem, powered by motors and wheels, is controlled by the Raspberry Pi, which will enable precise movements based on sensor inputs during integration. This foundation sets the stage for efficient navigation and task execution, showcasing the potential of cost-effective robotic systems for structured indoor applications.
Students Names: Atheer A. Robelah, Noora H. Saker
Supervisor: Dr. Mohamed Sultan Mohamed Ali
Project Title: Smart Wearable Device for Alzheimer’s Patients for Home Care Providers.
Abstract:
Alzheimer’s disease impairs memory and cognition, causing patients to wander and get lost, which poses significant safety risks for both patients and caregivers. Current monitoring systems, such as Global Positioning System trackers and smartphone applications, are limited in functionality, coverage, and reliability, highlighting the need for a comprehensive solution. This project proposes a smart wearable device designed to improve patient safety through real-time monitoring, location tracking, fall detection, and secure caregiver communication. The system comprises three interconnected subsystems: a wearable device equipped with sensors for fall detection, location tracking, and device removal detection; an edge computing unit for real-time data processing using threshold-based or machine learning algorithms; and a mobile application that provides caregivers with real-time alerts, patient status updates, and location data. Employing Internet of Things technology, the system ensures seamless communication and timely notifications. Key features include a secure locking mechanism to prevent device removal, low-power operation for extended battery life, and robust algorithms for accurate fall detection and patient posture recognition with at least accuracy of 95%.
Students Names: Sara N. Abdel-Hadi, Shaima M. Sadeghi, Tarteel B. Gaily
Supervisor: Dr. Faycal Bensaali