Class Schedule

AY2020-2021, Not Offered

Module Outline

This course introduces machine learning methods and their applications for data analytics. Students taking this course will learn modern machine learning techniques including classification, regression and generative models and algorithms as well as how to apply them to data analytics. The course starts with machine learning basics and some classical machine learning methods, followed by supervised and unsupervised data clustering, data dimensionally reduction for visualization and data classification. The students are expected to have solid background knowledge on calculus, linear algebra, probability and basic statistics (matlab or python).

Class Schedule

AY2020-2021, Not Offered

Module Outline

This module will introduce the students to the fundamentals for securing cyber physical systems and industries reliant on information and communication technologies. The students will be exposed to the key technologies behind cyber physical systems and Industry 4.0 and their security vulnerabilities. Tools and techniques for protecting against these vulnerabilities will be introduced and the students will also work with data from case studies from security breached in industrial systems to gain hand on experience in detecting and defending against cyber-attacks. The module will use practical systems such as smart grids for illustrating the concepts and as test cases.

Class Schedule

AY2020-2021, Not Offered

Module Outline

This module provides the necessary fundamentals in power systems modelling and analysis. Power system simulation involves modeling power generation equipment, planning the integration of power plants and renewable generation sources onto the electric grid, and performing power flow optimization. Students will learn how mathematical approaches and simulation tools are used for the design, modeling, planning, and analysis of power distribution grids. Students will gain hands-on experience on simulation and analysis of power systems with embedded generation, and visualization of large power distribution systems.

Class Schedule

AY2020-2021, Not Offered

Module Outline

When synchronous generators in an interconnected grid are subjected to a disturbance, exchange of energy among generators occurs to minimize the power imbalances. Such dynamics are captured in the form of electromechanical oscillations, and are inherent to power grids. From the utility perspective, poorly damped oscillations can trigger rotor-angle instability and cause wide-area blackouts. Hence, real-time assessment using advanced sensors like Phasor Measurement Unit (PMU) are used to improve grid stability. Additionally, proper design of the excitation system in a synchronous generator is critical. This module provides a holistic view of identifying and mitigating stability issues among synchronous generators.

Class Schedule

SEMESTER 2, AY2020-2021

Module Outline

Smart grids will be the energy delivery systems of the future. A smart grid is an electrical grid that can monitor, predict, and intelligently respond to the behaviour of all electric power suppliers and consumers connected to it in order to deliver reliable and sustainable electricity services as efficiently as possible. This module presents various smart grid architectures, functions of all its components like distributed energy sources, communication and measurement technology, energy management systems, operation management systems etc.

Lectured By:

Prof Dipti Srinivasan