Big Data Analytics For Smart Grid

By Dr. Ritula Thakur | National Institute of Technical Teachers Training & Research, Chandigarh

Learners enrolled: 1747

With the fast development of digital technology and cloud computing, more and more data are produced through digital equipment and sensors, as well as through human activities and communications. The collected data are mounting at an exponential rate and the structure of them is also becoming much more complicated. The processing and analysis method of these large volume data is a new challenge but also an opportunity with the concept of “big data”.

This course explores the usage of open source software python for demonstration of usage of big data in smart grid. It begins with the importance of big data analysis in smart grid, intelligent data collection devices followed by machine learning and deep learning algorithms used in data analytics for smart grid.

INTENDED AUDIENCE: Undergraduate students, Postgraduate students, research scholars, faculties of technical institutes, Industrial professionals.

PREREQUISITES: Basics of Power Systems, Basic knowledge of statistics.

INDUSTRY SUPPORT: The course is organised in Collaboration with eminent Industries such as Opal-RT and IBM.

Summary

Course Status :	Completed
Course Type :	Core
Duration :	8 weeks
Category :	Annual Refresher Programme in Teaching (ARPIT)
Level :	None
Start Date :	01 Dec 2020
End Date :	31 Mar 2021
Enrollment Ends :	31 Dec 2020
Exam Date :	10 Apr 2021 IST

Note: This exam date is subject to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout

Week1: Need of Data Analysis in Smart Grid
Week2: Intelligent Data Collection Devices in Smart Grid

Week3: Data Science Pertaining to Smart Grid Analytics

Week4: Tools for Big Data Analytics

Week5: Conventional Machine Learning Algorithms for Data Analytics

Week6: Advanced Machine Learning Algorithms for Data Analytics

Week7: Big Data Analytics for Smart Grid- Case Studies

Week8: Cloud and edge computing for Big data analytics

Books and references

1. Smart Grid: Fundamentals of Design and Analysis, 1st Edition, Wiley- IEEE Press.

2. Python Programming, 3rd Edition, John Zelle and Michael Smith, Franklin Beedle & Associates Inc.

3. Introduction to Machine Learning with Python, Andreas C. Mueller and Sarah Guido, O'Reilly Media, Inc.

Instructor bio

Dr. Ritula Thakur

National Institute of Technical Teachers Training & Research, Chandigarh

Dr. Ritula Thakur received B.E degree in Electrical engineering with Honours, M.E. degree in Power systems with distinction and Ph.D from Panjab University, Chandigarh.

Currently, she is working as Associate Professor at National Institute of Technical Teachers Training and Research, Chandigarh, India. Dr. Thakur has also worked as Visiting Scholar in Richard Russel Research Laboratory, Athens, USA.

Her research interests are in the areas of AI in Power Systems, Micro Grid and Smart Grid, Embedded systems and Microcontrollers, Electrical Engineering and Information Technology in Agriculture, Quality Analysis and Detection Technology in Food Materials Sensors and Instrumentation, Power Systems, Power Quality, PLC and SCADA,. She has successfully coordinated two ARPIT courses on SWAYAM portal in the field of Smart Grid. She has around 150 papers in various Conferences and Journals.

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