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Fees
₹5,00,000
Placement
94.5%
Avg Package
₹6,50,000
Highest Package
₹9,50,000
Fees
₹5,00,000
Placement
94.5%
Avg Package
₹6,50,000
Highest Package
₹9,50,000
Seats
120
Students
1,200
Seats
120
Students
1,200
| Semester | Course Code | Full Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | MA101 | Calculus I | 3-1-0-4 | - |
| 1 | PH101 | Physics I | 3-1-0-4 | - |
| 1 | CH101 | Chemistry I | 3-1-0-4 | - |
| 1 | EC101 | Introduction to Electrical Engineering | 2-0-2-4 | - |
| 1 | CS101 | Computer Programming | 3-0-2-5 | - |
| 1 | ME101 | Engineering Mechanics | 3-1-0-4 | - |
| 1 | HS101 | English for Communication | 2-0-0-2 | - |
| 2 | MA201 | Calculus II | 3-1-0-4 | MA101 |
| 2 | PH201 | Physics II | 3-1-0-4 | PH101 |
| 2 | EC201 | Circuit Analysis | 3-1-0-4 | - |
| 2 | EC202 | Electromagnetic Fields | 3-1-0-4 | - |
| 2 | CS201 | Data Structures and Algorithms | 3-0-2-5 | CS101 |
| 2 | ME201 | Mechanics of Materials | 3-1-0-4 | ME101 |
| 3 | EC301 | Signals and Systems | 3-1-0-4 | MA201, EC201 |
| 3 | EC302 | Electronic Devices and Circuits | 3-1-0-4 | - |
| 3 | EC303 | Digital Logic Design | 3-1-0-4 | - |
| 3 | EC304 | Control Systems | 3-1-0-4 | - |
| 3 | EC305 | Power Electronics | 3-1-0-4 | - |
| 3 | CS301 | Object-Oriented Programming with C++ | 3-0-2-5 | CS201 |
| 4 | EC401 | Microprocessors and Microcontrollers | 3-1-0-4 | - |
| 4 | EC402 | Communication Systems | 3-1-0-4 | - |
| 4 | EC403 | Power Generation and Distribution | 3-1-0-4 | - |
| 4 | EC404 | Industrial Electronics | 3-1-0-4 | - |
| 4 | EC405 | Antennas and Wave Propagation | 3-1-0-4 | - |
| 4 | CS401 | Database Management Systems | 3-0-2-5 | CS301 |
| 5 | EC501 | Power System Analysis | 3-1-0-4 | - |
| 5 | EC502 | Electrical Machines | 3-1-0-4 | - |
| 5 | EC503 | Advanced Control Systems | 3-1-0-4 | - |
| 5 | EC504 | Renewable Energy Sources | 3-1-0-4 | - |
| 5 | EC505 | Digital Signal Processing | 3-1-0-4 | - |
| 5 | CS501 | Computer Networks | 3-0-2-5 | CS401 |
| 6 | EC601 | Embedded Systems | 3-1-0-4 | - |
| 6 | EC602 | Smart Grid Technologies | 3-1-0-4 | - |
| 6 | EC603 | Industrial Automation | 3-1-0-4 | - |
| 6 | EC604 | RF and Microwave Engineering | 3-1-0-4 | - |
| 6 | EC605 | Optimization Techniques | 3-1-0-4 | - |
| 6 | CS601 | Software Engineering | 3-0-2-5 | CS501 |
| 7 | EC701 | Advanced Power Electronics | 3-1-0-4 | - |
| 7 | EC702 | Power System Protection | 3-1-0-4 | - |
| 7 | EC703 | Artificial Intelligence in Electrical Systems | 3-1-0-4 | - |
| 7 | EC704 | Energy Storage Systems | 3-1-0-4 | - |
| 7 | EC705 | Advanced Signal Processing | 3-1-0-4 | - |
| 7 | CS701 | Machine Learning Fundamentals | 3-0-2-5 | CS601 |
| 8 | EC801 | Final Year Project | 4-0-0-8 | - |
| 8 | EC802 | Project Management | 2-0-0-2 | - |
| 8 | EC803 | Professional Ethics and Social Responsibility | 2-0-0-2 | - |
| 8 | EC804 | Research Methodology | 2-0-0-2 | - |
Advanced Power Electronics is a departmental elective that explores the principles and applications of power electronic converters, including DC-DC converters, AC-DC rectifiers, inverters, and motor drives. The course emphasizes design methodologies, simulation techniques, and real-world implementation challenges.
Power System Protection delves into protective relaying schemes for transmission lines, transformers, generators, and busbars. Students learn about fault analysis, relay settings, coordination principles, and modern digital protection systems used in utility companies.
Artificial Intelligence in Electrical Systems introduces students to AI-based approaches in power system optimization, load forecasting, and smart grid control. The course covers neural networks, genetic algorithms, fuzzy logic, and machine learning applications in electrical engineering domains.
Energy Storage Systems focuses on battery technologies, supercapacitors, flywheels, and other energy storage solutions. It includes discussions on energy conversion efficiency, system integration, charging strategies, and economic modeling of storage systems.
Advanced Signal Processing explores advanced topics such as wavelet transforms, adaptive filtering, beamforming, and spectral estimation techniques. The course provides hands-on experience with MATLAB-based implementations and real-world signal processing applications.
Smart Grid Technologies covers the architecture, operation, and control of modern electrical grids with distributed generation, demand response programs, and grid automation systems. Students engage in case studies involving smart metering, microgrids, and renewable energy integration.
Industrial Automation introduces programmable logic controllers (PLCs), SCADA systems, sensor networks, and industrial communication protocols like Modbus, EtherCAT, and Profinet. Practical labs involve designing and implementing automation solutions for manufacturing processes.
RF and Microwave Engineering studies electromagnetic wave propagation, transmission lines, microwave components, and antennas used in wireless communications. The course includes laboratory sessions on network analyzers, spectrum analyzers, and microwave measurement techniques.
Digital Signal Processing is a core subject that covers discrete-time signal processing, filter design, FFT algorithms, and DSP processors. Students learn to implement digital filters using software tools and hardware platforms like ARM Cortex-M series microcontrollers.
Optimization Techniques addresses linear programming, integer programming, dynamic programming, and nonlinear optimization methods used in electrical engineering problems. The course includes practical applications such as power system optimization and resource allocation in telecommunications.
The department's approach to project-based learning is designed to bridge the gap between theory and practice by engaging students in meaningful, real-world challenges. Projects are structured into two phases: mini-projects in the early semesters and a final-year capstone project.
Mini-projects are typically undertaken during the second and third years. These projects span 8-12 weeks and involve small teams of 3-5 students working under faculty supervision. The goal is to apply fundamental concepts learned in lectures to solve specific problems, thereby reinforcing classroom learning and building teamwork skills.
Mini-projects are selected based on industry trends, faculty research interests, or student proposals submitted during the beginning of each academic year. Topics can range from designing a simple electronic circuit to developing an algorithm for image recognition in power systems. Each project must include a literature review, design phase, prototype development, testing procedures, and final report.
The final-year thesis/capstone project is a comprehensive endeavor that spans the entire eighth semester. Students work closely with faculty mentors on original research or applied projects aligned with current industry needs. The project involves extensive literature survey, methodology development, experimentation, data analysis, and documentation.
Project selection process begins in the sixth semester when students are encouraged to propose ideas based on their interests and career goals. Faculty mentors are assigned based on expertise matching, ensuring guidance that supports both academic rigor and practical relevance. Regular progress meetings, milestone reviews, and peer feedback sessions ensure continuous improvement throughout the project lifecycle.
