Comprehensive Course Structure
The Electrical Engineering program at F S University Firozabad is structured over eight semesters to ensure a progressive and comprehensive learning experience. The curriculum balances foundational knowledge with advanced specialization, integrating theory, practical application, and industry relevance.
Semester-wise Course Listing
| Year | Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|---|
| Year 1 | Semester I | PHYS101 | Physics for Engineers | 3-1-0-4 | None |
| MATH101 | Calculus and Analytical Geometry | 4-0-0-4 | None | ||
| ENG101 | English for Technical Communication | 3-0-0-3 | None | ||
| Semester II | CHEM101 | Chemistry for Engineers | 3-1-0-4 | None | |
| MATH102 | Linear Algebra and Differential Equations | 4-0-0-4 | MATH101 | ||
| EG101 | Engineering Graphics | 2-1-0-3 | None | ||
| Year 2 | Semester III | EC101 | Circuit Analysis | 3-1-0-4 | MATH102, PHYS101 |
| EE101 | Electromagnetic Fields | 3-1-0-4 | MATH102, PHYS101 | ||
| CS101 | Introduction to Programming | 3-0-2-4 | None | ||
| Semester IV | EC102 | Signals and Systems | 3-1-0-4 | EC101, MATH102 | |
| EE102 | Electronics Devices and Circuits | 3-1-0-4 | EC101, PHYS101 | ||
| CS102 | Data Structures and Algorithms | 3-0-2-4 | CS101 | ||
| Year 3 | Semester V | EE201 | Power System Analysis | 3-1-0-4 | EC101, EE101 |
| EE202 | Control Systems | 3-1-0-4 | EC102, MATH102 | ||
| EE203 | Digital Electronics | 3-1-0-4 | EE102 | ||
| Semester VI | EE204 | Communication Systems | 3-1-0-4 | EC102, EE102 | |
| EE205 | Microprocessors and Microcontrollers | 3-1-0-4 | EC101, EE102 | ||
| EE206 | Electrical Machines | 3-1-0-4 | EC101, EE101 | ||
| Year 4 | Semester VII | EE301 | Renewable Energy Systems | 3-1-0-4 | EE201, EE206 |
| EE302 | Power Electronics | 3-1-0-4 | EC101, EE102 | ||
| EE303 | Advanced Control Systems | 3-1-0-4 | EE202 | ||
| Semester VIII | EE304 | Elective Courses (Choose 2) | 3-1-0-4 | EE201, EE202, EE203, EE204, EE205, EE206 | |
| EE305 | Capstone Project | 0-0-6-8 | All previous courses | ||
| EE306 | Industrial Training | 0-0-0-2 | None |
Detailed Departmental Elective Courses
Departmental electives allow students to explore advanced topics aligned with their interests and career goals. These courses are designed to provide depth in specialized areas while maintaining flexibility for interdisciplinary learning.
Power System Protection
This course introduces students to the principles and practices of power system protection, including relay characteristics, fault analysis, and protective device coordination. Students learn how to design protection schemes for transmission and distribution systems using industry-standard tools like ETAP and PSCAD/EMTDC. The course includes laboratory sessions where students simulate protection scenarios and analyze real-time data from power plants.
Industrial Automation and Control
Students gain practical knowledge of industrial automation technologies, including PLC programming, SCADA systems, sensor networks, and robotics. The course emphasizes hands-on learning through lab experiments and project work involving actual manufacturing processes. Students also study automation standards such as IEC 61508 and ISO 27001.
Advanced Digital Signal Processing
This advanced elective explores modern techniques in digital signal processing, including wavelet transforms, adaptive filtering, and spectral estimation methods. Students implement algorithms using MATLAB and Python, applying them to audio and image processing tasks. The course also covers real-time DSP applications in biomedical engineering and telecommunications.
Smart Grid Technologies
Smart grids represent the future of power systems, integrating renewable energy sources, demand response technologies, and intelligent monitoring systems. This course covers topics such as grid stability analysis, voltage regulation, load forecasting, and cybersecurity in smart grids. Students work on projects involving smart meter deployment, grid optimization algorithms, and distributed energy resource management.
Wireless Sensor Networks
Students learn about the design, implementation, and deployment of wireless sensor networks for environmental monitoring, industrial automation, and healthcare applications. The course covers communication protocols like Zigbee, Bluetooth Low Energy, and LoRaWAN, along with network topology optimization and data fusion techniques. Lab sessions involve constructing sensor nodes and analyzing network performance metrics.
Electromagnetic Compatibility
This course focuses on ensuring that electronic systems operate without interference from electromagnetic sources. Topics include EMI/EMC design principles, shielding techniques, filtering methods, and regulatory compliance (FCC, CE Marking). Students conduct EMI measurements using spectrum analyzers and oscilloscopes, learning to troubleshoot common compatibility issues in real-world scenarios.
Energy Storage Systems
With the increasing adoption of renewable energy sources, understanding energy storage technologies becomes crucial. This course covers battery technologies (Li-ion, lead-acid, flow batteries), supercapacitors, and hybrid storage systems. Students study charging strategies, efficiency optimization, and system integration challenges. Practical sessions include battery testing procedures and simulation of energy management algorithms.
Photovoltaic System Design
This elective teaches students how to design and optimize photovoltaic (PV) systems for residential, commercial, and utility-scale applications. Topics include solar irradiance modeling, panel efficiency analysis, inverter selection, and grid integration considerations. Students work on full-system designs using PVsyst and HOMER software tools, considering factors like site location, shading, and maintenance requirements.
Renewable Energy Integration
As renewable energy sources become more prevalent, integrating them into existing power systems presents unique challenges. This course covers wind power generation, solar power forecasting, grid stability, and policy frameworks supporting clean energy transitions. Students engage in case studies of successful integration projects and model different scenarios using MATLAB/Simulink.
Embedded Systems Architecture
This advanced elective explores the architecture and design of embedded systems used in automotive, aerospace, and consumer electronics. Students learn about microcontroller architectures, real-time operating systems (RTOS), memory management, and system-on-chip (SoC) designs. Practical work includes developing firmware for ARM Cortex-M series processors using Keil MDK and IAR Embedded Workbench.
Project-Based Learning Philosophy
The department places significant emphasis on project-based learning as a cornerstone of engineering education. Projects are integrated throughout the curriculum to reinforce theoretical concepts, develop practical skills, and encourage innovation.
Mini-Projects in Years 1-2
During the first two years, students engage in mini-projects that introduce them to design thinking, problem-solving methodologies, and collaborative teamwork. These projects are typically completed in small groups of 3-5 students and focus on applying fundamental concepts learned in core courses. Examples include designing a simple DC motor controller, building a basic electronic circuit, or analyzing signal characteristics using oscilloscopes.
Final-Year Thesis/Capstone Project
The capstone project is the culmination of the undergraduate experience, requiring students to apply comprehensive knowledge to solve complex engineering problems. Students select projects based on their interests and career aspirations, working under the guidance of faculty mentors from the department or industry partners. The project involves literature review, conceptual design, simulation, prototype development, testing, and documentation.
Project Selection Process
Students begin selecting capstone projects in their third year, with options ranging from industry-sponsored initiatives to independent research proposals. Faculty mentors are assigned based on project relevance and student preferences. The department maintains a database of potential project ideas sourced from current research areas, industry requirements, and societal needs.
Evaluation Criteria
Projects are evaluated based on multiple criteria including technical merit, innovation, presentation quality, peer review, and final deliverables. A panel of faculty members and industry experts conducts reviews at various stages of the project lifecycle. Students present their progress in interim reports and final presentations, demonstrating communication skills and professional maturity.