Comprehensive Course List and Structure
| Semester | Course Code | Full Course Title | Credit (L-T-P-C) | Prerequisite |
|---|---|---|---|---|
| Semester I | EE101 | Engineering Mathematics I | 3-1-0-4 | - |
| EE102 | Physics for Electrical Engineering | 3-1-0-4 | - | |
| EE103 | Chemistry for Engineers | 3-1-0-4 | - | |
| EE104 | Introduction to Electrical Engineering | 2-0-0-2 | - | |
| EE105 | Programming for Engineers | 2-0-2-3 | - | |
| EE106 | Engineering Drawing & Graphics | 1-0-2-2 | - | |
| EE107 | Workshop Practices | 0-0-3-2 | - | |
| EE108 | Physical Education & Sports | 0-0-0-2 | - | |
| EE109 | English Communication Skills | 2-0-0-2 | - | |
| EE110 | Engineering Ethics & Professionalism | 2-0-0-2 | - | |
| Semester II | EE201 | Engineering Mathematics II | 3-1-0-4 | EE101 |
| EE202 | Circuit Analysis | 3-1-0-4 | EE102 | |
| EE203 | Electromagnetic Fields | 3-1-0-4 | EE102 | |
| EE204 | Digital Logic Design | 3-1-0-4 | EE105 | |
| EE205 | Analog Electronics | 3-1-0-4 | EE202 | |
| EE206 | Data Structures & Algorithms | 3-1-0-4 | EE105 | |
| EE207 | Signals and Systems | 3-1-0-4 | EE201 | |
| EE208 | Electrical Measurements | 2-0-2-3 | EE202 | |
| EE209 | Workshop II | 0-0-3-2 | - | |
| EE210 | Human Values & Professional Ethics | 2-0-0-2 | - | |
| Semester III | EE301 | Engineering Mathematics III | 3-1-0-4 | EE201 |
| EE302 | Network Theory | 3-1-0-4 | EE202 | |
| EE303 | Control Systems | 3-1-0-4 | EE207 | |
| EE304 | Digital Signal Processing | 3-1-0-4 | EE207 | |
| EE305 | Microprocessors & Microcontrollers | 3-1-0-4 | EE204 | |
| EE306 | Electromagnetic Waves & Transmission Lines | 3-1-0-4 | EE203 | |
| EE307 | Probability & Statistics for Engineers | 3-1-0-4 | EE201 | |
| EE308 | Electrical Machines I | 3-1-0-4 | EE202 | |
| EE309 | Computer Organization & Architecture | 3-1-0-4 | EE206 | |
| EE310 | Communication Systems | 3-1-0-4 | EE207 | |
| Semester IV | EE401 | Power System Analysis | 3-1-0-4 | EE302 |
| EE402 | Electrical Machines II | 3-1-0-4 | EE308 | |
| EE403 | Power Electronics | 3-1-0-4 | EE305 | |
| EE404 | Measurement & Instrumentation | 3-1-0-4 | EE208 | |
| EE405 | Power System Protection | 3-1-0-4 | EE401 | |
| EE406 | Renewable Energy Sources | 3-1-0-4 | EE207 | |
| EE407 | Industrial Drives & Control | 3-1-0-4 | EE303 | |
| EE408 | Embedded Systems | 3-1-0-4 | EE305 | |
| EE409 | Advanced Control Theory | 3-1-0-4 | EE303 | |
| EE410 | Optimization Techniques | 3-1-0-4 | EE207 | |
| Semester V | EE501 | Advanced Power System Protection | 3-1-0-4 | EE405 |
| EE502 | Smart Grid Technologies | 3-1-0-4 | EE401 | |
| EE503 | Modern Control Systems | 3-1-0-4 | EE409 | |
| EE504 | Signal Processing Applications | 3-1-0-4 | EE404 | |
| EE505 | Artificial Intelligence in Electrical Engineering | 3-1-0-4 | EE206 | |
| EE506 | Power Quality and Harmonics | 3-1-0-4 | EE401 | |
| EE507 | Research Methodology | 2-0-2-3 | - | |
| EE508 | Electrical Engineering Project I | 0-0-6-4 | - | |
| Semester VI | EE601 | Advanced Power Electronics | 3-1-0-4 | EE403 |
| EE602 | Renewable Energy Systems Design | 3-1-0-4 | EE406 | |
| EE603 | Power System Dynamics & Stability | 3-1-0-4 | EE401 | |
| EE604 | Industrial Automation & PLC | 3-1-0-4 | EE307 | |
| EE605 | Machine Learning for Electrical Applications | 3-1-0-4 | EE505 | |
| EE606 | IoT & Wireless Networks | 3-1-0-4 | EE410 | |
| EE607 | Electrical Engineering Project II | 0-0-6-4 | EE508 | |
| EE608 | Electrical Engineering Lab I | 0-0-3-2 | - | |
| Semester VII | EE701 | Capstone Project Preparation | 2-0-4-3 | EE508 |
| EE702 | Advanced Control Systems | 3-1-0-4 | EE503 | |
| EE703 | Energy Storage Systems | 3-1-0-4 | EE602 | |
| EE704 | Industrial Robotics | 3-1-0-4 | EE503 | |
| EE705 | Research Paper Writing & Presentation | 2-0-2-3 | - | |
| EE706 | Electrical Engineering Lab II | 0-0-3-2 | EE608 | |
| Semester VIII | EE801 | Final Year Thesis/Capstone Project | 0-0-12-8 | EE701 |
| EE802 | Electrical Engineering Internship | 0-0-6-4 | - | |
| EE803 | Professional Practices & Project Management | 2-0-2-3 | - | |
| EE804 | Entrepreneurship in Electrical Engineering | 2-0-2-3 | - | |
| EE805 | Electrical Engineering Workshop | 0-0-6-4 | - | |
| EE806 | Final Project Defense & Evaluation | 0-0-3-2 | EE801 |
Detailed Departmental Elective Courses
The following are advanced departmental elective courses offered in the Electrical Engineering program, each with specific learning objectives and relevance to industry needs.
- Advanced Power Electronics: This course covers advanced topics in power electronic converters, including three-phase inverters, resonant converters, and wide-bandgap semiconductor devices. It aims to provide students with a deep understanding of modern power conversion techniques used in renewable energy systems and electric vehicles.
- Renewable Energy Systems Design: Designed to equip students with the knowledge required for designing and optimizing renewable energy systems such as solar photovoltaic arrays, wind turbines, and hydroelectric plants. Students will learn about system integration, performance evaluation, and economic analysis of different technologies.
- Power System Dynamics & Stability: Focuses on modeling and analyzing dynamic behavior of power systems under disturbances. Topics include small-signal stability analysis, transient stability, and control strategies for maintaining grid stability during contingencies.
- Industrial Automation & PLC: Introduces students to programmable logic controllers (PLCs) and industrial automation principles. The course includes hands-on training on ladder logic programming, HMI design, and integration of sensors and actuators in automated systems.
- Machine Learning for Electrical Applications: Combines machine learning techniques with electrical engineering concepts to solve complex problems in power systems, signal processing, and control theory. Students will learn how to apply neural networks, decision trees, and clustering algorithms to real-world scenarios.
- IoT & Wireless Networks: Explores the design and implementation of wireless communication protocols for IoT applications. The course covers sensor networks, wireless standards (WiFi, Bluetooth, LoRaWAN), and network security aspects relevant to smart cities and industrial IoT deployments.
- Energy Storage Systems: Focuses on the fundamentals of energy storage technologies including batteries, supercapacitors, and compressed air systems. Students will analyze their performance characteristics, design considerations, and integration strategies within power grids and electric vehicle charging infrastructure.
- Industrial Robotics: Provides an overview of robotics technology used in manufacturing and automation environments. Students will study robot kinematics, control systems, sensor integration, and programming techniques for industrial applications.
- Smart Grid Technologies: Covers the evolution of traditional power grids into smart grids using advanced communication and control technologies. The course explores topics like demand response programs, grid automation, cybersecurity in smart grids, and distributed energy resources management.
- Power Quality and Harmonics: Investigates issues related to power quality degradation caused by nonlinear loads and disturbances in electrical systems. Students will learn methods for analyzing harmonic distortion, designing filters, and implementing corrective measures.
Project-Based Learning Philosophy
The Electrical Engineering program at The Aryavart International University North Tripura places a strong emphasis on project-based learning as a core pedagogical strategy. This approach is designed to foster creativity, innovation, and practical problem-solving skills among students.
From the very first semester, students are introduced to small-scale projects that reinforce classroom concepts through hands-on experience. These mini-projects are carefully structured to allow students to explore different aspects of electrical engineering while developing essential teamwork and communication skills.
The final-year thesis/capstone project represents the culmination of the student's academic journey. Students are expected to identify a real-world problem, conduct literature review, propose a solution using appropriate tools and methodologies, implement it in a laboratory setting, and present their findings professionally.
Project selection is facilitated through faculty mentorship sessions where students can discuss their interests and capabilities with potential advisors. Each student is assigned a faculty mentor who guides them throughout the project lifecycle, providing technical support, feedback, and professional development advice.
The evaluation criteria for these projects are comprehensive and include aspects such as innovation, feasibility, technical depth, presentation quality, and impact assessment. Students are encouraged to publish their work in journals or present at conferences, thereby enhancing their visibility in the academic and industrial communities.