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Pune, Maharashtra, India

Duration

4 Years

Electrical

Government Polytechnic Khatima
Duration
4 Years
Electrical UG OFFLINE

Duration

4 Years

Electrical

Government Polytechnic Khatima
Duration
Apply

Fees

₹3,50,000

Placement

94.5%

Avg Package

₹6,50,000

Highest Package

₹18,00,000

OverviewAdmissionsCurriculumFeesPlacements
4 Years
Electrical
UG
OFFLINE

Fees

₹3,50,000

Placement

94.5%

Avg Package

₹6,50,000

Highest Package

₹18,00,000

Seats

120

Students

1,200

ApplyCollege

Seats

120

Students

1,200

Curriculum

Course Structure Overview

The Electrical program at Govt Polytechnic Khatima is structured over eight semesters, with a carefully balanced blend of foundational courses, core engineering subjects, departmental electives, and practical laboratory work. The curriculum emphasizes both theoretical understanding and hands-on application, preparing students for real-world challenges in the field of electrical engineering.

SemesterCourse CodeCourse TitleCredit Structure (L-T-P-C)Pre-requisites
1MATH101Calculus I3-1-0-4-
1PHYS101Physics I3-1-0-4-
1ENGL101English Communication Skills2-0-0-2-
1EC101Introduction to Electrical Engineering2-0-0-2-
1CSE101Programming Fundamentals2-0-0-2-
2MATH102Calculus II3-1-0-4MATH101
2PHYS102Physics II3-1-0-4PHYS101
2EC102Circuit Analysis3-1-0-4-
2ELEC101Electrical Measurements2-0-0-2-
2EE101Basics of Electrical Machines2-0-0-2-
3MATH201Differential Equations3-1-0-4MATH102
3PHYS201Electromagnetic Fields3-1-0-4PHYS102
3EC201Network Analysis3-1-0-4EC102
3ELEC201Power System Basics2-0-0-2-
3EE201Electrical Machines I2-0-0-2-
4MATH202Probability and Statistics3-1-0-4MATH201
4PHYS202Optics and Modern Physics3-1-0-4PHYS201
4EC301Electronic Devices3-1-0-4EC201
4ELEC301Control Systems2-0-0-2-
4EE301Electrical Machines II2-0-0-2EE201
5MATH301Numerical Methods3-1-0-4MATH202
5EC401Digital Logic Design3-1-0-4EC301
5ELEC401Power Electronics2-0-0-2-
5EE401Microprocessors and Microcontrollers2-0-0-2-
5EE501Advanced Electrical Machines2-0-0-2EE301
6EC501Signals and Systems3-1-0-4EC401
6ELEC501Renewable Energy Systems2-0-0-2-
6EE601Embedded Systems2-0-0-2-
6EE701Power System Protection2-0-0-2-
7EC601Communication Systems3-1-0-4EC501
7ELEC601Smart Grids2-0-0-2-
7EE801Industrial Automation2-0-0-2-
7EE901Artificial Intelligence in Electrical Applications2-0-0-2-
8EC701Capstone Project3-1-0-4All previous semesters
8ELEC701Research Methodology2-0-0-2-
8EE1001Project Presentation and Viva Voce2-0-0-2-

Advanced Departmental Electives

Departmental electives offer students the opportunity to specialize in niche areas of interest. These courses are designed to provide depth and breadth beyond the core curriculum, preparing students for advanced roles in industry or further academic pursuits.

The course 'Renewable Energy Systems' delves into solar photovoltaic technology, wind turbine design, hydroelectric generation, and energy storage solutions. Students study real-world case studies and participate in hands-on experiments using actual renewable energy systems.

'Power System Protection' explores the principles of relay operation, fault analysis, and protective relaying schemes in power systems. The course includes simulation exercises using industry-standard software like ETAP and DIgSILENT.

'Embedded Systems' introduces students to microcontroller architecture, real-time operating systems, sensor integration, and embedded C programming. Projects involve building functional prototypes for applications in IoT and automation.

'Control Engineering' covers classical and modern control theory, state-space methods, digital control systems, and system identification techniques. Students gain proficiency in MATLAB/Simulink-based modeling and simulation tools.

'Signal Processing' focuses on digital signal processing algorithms, filter design, and spectral analysis techniques. The course includes practical sessions using software like Python with NumPy/SciPy libraries and MATLAB.

'VLSI Design' provides an in-depth understanding of integrated circuit design processes, logic synthesis, layout design, and verification methods. Students use industry-standard tools such as Cadence and Mentor Graphics for design implementation.

'Smart Grids' examines the integration of modern technologies into traditional power grids to improve efficiency, reliability, and sustainability. Topics include grid automation, demand response systems, and energy management strategies.

'Industrial Automation' focuses on the application of control systems and sensors in manufacturing environments. Students learn about programmable logic controllers (PLCs), industrial communication protocols, and robotic integration.

'Artificial Intelligence in Electrical Applications' introduces students to machine learning algorithms and neural networks applied to electrical systems. The course covers predictive maintenance, energy optimization, and intelligent control systems using Python and TensorFlow.

Project-Based Learning Approach

The department strongly advocates for project-based learning as a cornerstone of the educational experience. This approach encourages students to apply theoretical knowledge in practical scenarios, fostering innovation and problem-solving skills.

Mini-projects are undertaken during the third and fourth semesters, with each project lasting approximately 6-8 weeks. Students form teams and select projects based on their interests and faculty expertise. Each team receives guidance from a designated faculty mentor who ensures academic rigor and practical relevance.

The final-year thesis/capstone project is a comprehensive endeavor that spans the entire eighth semester. Students work closely with faculty advisors to define research questions, conduct literature reviews, design experiments, and present findings. Projects are evaluated based on originality, technical depth, and contribution to the field.

Project selection is facilitated through an online portal where students can browse available projects posted by faculty members. The selection process considers student preferences, project requirements, and availability of resources.