Comprehensive Course Structure
The Electrical Engineering program at Shri Khushal Das University Hanumangarh is structured over 8 semesters, with a carefully balanced mix of core subjects, departmental electives, science electives, and laboratory work. This structure ensures that students build a strong foundation in the basics before progressing to advanced topics and specialized areas.
Year 1: Foundation Building
The first year focuses on building a solid foundation in mathematics, physics, and basic engineering principles. Students are introduced to fundamental concepts such as circuit analysis, basic electronics, and programming, laying the groundwork for more advanced topics.
Year 2: Core Engineering Principles
During the second year, students delve into core electrical engineering subjects such as electrical machines, power systems, and control systems. They also begin to explore specialized areas through elective courses, allowing them to identify their interests and strengths.
Year 3: Specialization and Practical Application
The third year marks a significant transition towards specialization. Students choose from various tracks such as Power Systems, Control Systems, Signal Processing, and Embedded Systems. They engage in advanced coursework, laboratory experiments, and project work that deepens their understanding of their chosen area.
Year 4: Capstone and Advanced Research
The fourth year is dedicated to advanced specialization and capstone projects. Students work on comprehensive projects that integrate their knowledge and skills, often in collaboration with industry partners. They are encouraged to pursue research, innovation, and entrepreneurship, with support from faculty mentors and industry experts.
Course Structure Overview
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | None |
| 1 | PHY101 | Physics for Engineers | 3-1-0-4 | None |
| 1 | CE101 | Introduction to Computer Engineering | 3-1-0-4 | None |
| 1 | EE101 | Basic Electrical Engineering | 3-1-0-4 | None |
| 1 | ME101 | Engineering Mechanics | 3-1-0-4 | None |
| 1 | CS101 | Programming and Problem Solving | 3-1-0-4 | None |
| 2 | ENG102 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | PHY102 | Electromagnetic Fields | 3-1-0-4 | PHY101 |
| 2 | EE102 | Circuit Analysis | 3-1-0-4 | EE101 |
| 2 | EE103 | Electrical Machines | 3-1-0-4 | EE102 |
| 2 | EE104 | Electromagnetic Fields and Waves | 3-1-0-4 | PHY102 |
| 2 | CS102 | Data Structures and Algorithms | 3-1-0-4 | CS101 |
| 3 | EE201 | Power Systems Analysis | 3-1-0-4 | EE103 |
| 3 | EE202 | Control Systems | 3-1-0-4 | EE102 |
| 3 | EE203 | Signal Processing | 3-1-0-4 | ENG102 |
| 3 | EE204 | Electronics Devices and Circuits | 3-1-0-4 | EE101 |
| 3 | EE205 | Power Electronics | 3-1-0-4 | EE103 |
| 3 | EE206 | Embedded Systems | 3-1-0-4 | CS102 |
| 4 | EE301 | Advanced Power Systems | 3-1-0-4 | EE201 |
| 4 | EE302 | Modern Control Systems | 3-1-0-4 | EE202 |
| 4 | EE303 | Digital Signal Processing | 3-1-0-4 | EE203 |
| 4 | EE304 | Microprocessors and Microcontrollers | 3-1-0-4 | EE204 |
| 4 | EE305 | Renewable Energy Systems | 3-1-0-4 | EE205 |
| 4 | EE306 | Communication Systems | 3-1-0-4 | EE203 |
| 5 | EE401 | Power System Protection | 3-1-0-4 | EE301 |
| 5 | EE402 | Advanced Control Theory | 3-1-0-4 | EE302 |
| 5 | EE403 | Image and Video Processing | 3-1-0-4 | EE303 |
| 5 | EE404 | Design of Electronic Circuits | 3-1-0-4 | EE304 |
| 5 | EE405 | Smart Grid Technologies | 3-1-0-4 | EE305 |
| 5 | EE406 | Wireless Communication | 3-1-0-4 | EE306 |
| 6 | EE501 | Power System Dynamics | 3-1-0-4 | EE401 |
| 6 | EE502 | Advanced Signal Processing | 3-1-0-4 | EE402 |
| 6 | EE503 | Microelectronics and VLSI Design | 3-1-0-4 | EE404 |
| 6 | EE504 | Energy Storage Systems | 3-1-0-4 | EE405 |
| 6 | EE505 | Optimization in Electrical Systems | 3-1-0-4 | EE406 |
| 7 | EE601 | Research Methodology | 3-1-0-4 | EE501 |
| 7 | EE602 | Advanced Control Systems | 3-1-0-4 | EE502 |
| 7 | EE603 | Machine Learning in Electrical Engineering | 3-1-0-4 | EE503 |
| 7 | EE604 | Energy Conversion Systems | 3-1-0-4 | EE504 |
| 7 | EE605 | Smart Cities and IoT | 3-1-0-4 | EE505 |
| 8 | EE701 | Final Year Project | 3-1-0-4 | EE601 |
| 8 | EE702 | Capstone Project | 3-1-0-4 | EE602 |
| 8 | EE703 | Industry Internship | 3-1-0-4 | EE603 |
| 8 | EE704 | Entrepreneurship and Innovation | 3-1-0-4 | EE604 |
| 8 | EE705 | Professional Development | 3-1-0-4 | EE605 |
Advanced Departmental Electives
The department offers a wide range of advanced departmental elective courses that allow students to explore specialized areas of interest. These courses are designed to provide in-depth knowledge and practical skills in specific domains of electrical engineering.
Power System Protection
This course focuses on the principles and practices of power system protection. Students learn about protective relaying, fault analysis, and protection schemes for different components of power systems. The course includes laboratory sessions where students simulate protection systems and analyze real-world scenarios.
Advanced Signal Processing
This course delves into advanced techniques in signal processing, including wavelet transforms, adaptive filtering, and spectral estimation. Students gain hands-on experience with digital signal processing tools and apply these techniques to real-world problems in audio, image, and biomedical signal processing.
Microelectronics and VLSI Design
This course covers the design and fabrication of integrated circuits and electronic systems. Students learn about semiconductor devices, circuit design, and VLSI design methodologies. The course includes practical sessions using CAD tools and fabrication processes, preparing students for careers in the semiconductor industry.
Energy Storage Systems
This course explores the design and implementation of energy storage systems, including batteries, supercapacitors, and other storage technologies. Students study energy storage applications in renewable energy systems, electric vehicles, and grid integration, with laboratory sessions involving battery testing and system design.
Optimization in Electrical Systems
This course introduces optimization techniques and their applications in electrical engineering. Students learn about linear and nonlinear programming, genetic algorithms, and other optimization methods. The course includes practical applications in power system optimization, control system design, and energy management.
Smart Cities and IoT
This course explores the integration of Internet of Things (IoT) technologies in smart cities. Students study sensor networks, data analytics, and smart infrastructure systems. The course includes projects involving smart traffic management, energy-efficient buildings, and environmental monitoring systems.
Machine Learning in Electrical Engineering
This course introduces machine learning techniques and their applications in electrical engineering. Students learn about neural networks, deep learning, and data mining, and apply these techniques to problems in power systems, signal processing, and control systems. The course includes hands-on sessions with machine learning frameworks and tools.
Energy Conversion Systems
This course covers the principles and applications of energy conversion systems, including power electronics, electric drives, and renewable energy systems. Students study energy conversion technologies and design systems for various applications, with laboratory sessions involving power electronics testing and system integration.
Advanced Control Systems
This course explores advanced control theory and its applications in modern engineering systems. Students study robust control, optimal control, and nonlinear control systems, with practical applications in robotics, aerospace, and industrial automation. The course includes laboratory sessions involving control system design and simulation.
Research Methodology
This course introduces students to research methodologies and scientific writing. Students learn about literature review, experimental design, data analysis, and thesis writing. The course prepares students for research projects and graduate studies, with practical sessions in research planning and presentation skills.
Project-Based Learning Philosophy
The department's philosophy on project-based learning is centered on providing students with hands-on experience and real-world problem-solving skills. The program emphasizes collaborative learning, where students work in teams to design, develop, and test engineering solutions.
Mini-Projects
Mini-projects are an integral part of the curriculum, starting from the second year. These projects are designed to reinforce theoretical concepts and develop practical skills. Students work on small-scale projects that simulate real-world engineering challenges, with guidance from faculty mentors.
Final-Year Thesis/Capstone Project
The final-year project is a comprehensive, individual or team-based endeavor that integrates all the knowledge and skills acquired during the program. Students select a topic of interest, conduct research, and develop a complete solution or system. The project is supervised by a faculty mentor and is evaluated based on innovation, technical depth, and presentation quality.
Project Selection and Mentorship
Students select their projects based on their interests and career aspirations. The department provides a list of potential topics and faculty mentors, and students can choose their preferred project and mentor. The mentorship process involves regular meetings, progress reviews, and feedback to ensure that students stay on track and achieve their goals.
Evaluation Criteria
The evaluation of projects is based on multiple criteria, including technical content, innovation, presentation, and documentation. Students are assessed through project reports, oral presentations, and practical demonstrations. The evaluation process is designed to provide constructive feedback and encourage continuous improvement.