Big Data Analytics in Power Systems

Webinar details

You will learn how cutting-edge analytics techniques can improve the management and operation of power grids. The webinar covers various topics, starting with an overview of big data analytics and its importance in power systems. It then delves into how data is collected, cleaned, and stored, ensuring it is reliable and scalable. Attendees will also explore different analysis methods, such as machine learning, used for predictive maintenance, demand forecasting, and real-time monitoring. Through real-world examples, they will see how these techniques enhance grid reliability and optimize energy distribution.

• The webinar will be recorded and will be sent out to registered attendees afterwards.
• A certificate of attendance will be provided to attendees who request one near the end of the live webinar session.
• Please note: the time stated on this event is in UTC. You will need to convert this to your own time zone.

Key takeaways from this webinar

Recent development in power system protection and control

• Participants will understand how advanced analytics can predict equipment failures and optimize maintenance schedules, thereby enhancing grid reliability.
• Attendees will learn how data-driven approaches enable utilities to optimize energy distribution, minimize wastage, and reduce costs.
• The importance of real-time monitoring and control enabled by big data analytics will be emphasized, empowering utilities to detect anomalies and ensure grid stability and resilience.

Related courses

This webinar/topic relates to our schools of Electrical Engineering and Industrial Automation, Instrumentation and Process Control particularly found in the following courses:

• Online – Bachelor of Science (Electrical Engineering)
• Online – Bachelor of Science (Industrial Automation Engineering)
• Online – Master of Engineering (Electrical Systems)
• Online – Master of Engineering (Industrial Automation)

To learn more about tuition fees, please click here.

About the presenter

Saeideh Sekhavat, EIT Lecturer & Research Assistant

Saeideh Sekhavat (Sadie) is a dedicated professional with a strong background in mathematics and a focus on leveraging artificial intelligence in healthcare. With a bachelor’s degree in Applied Mathematics and a graduate degree in Systems Analysis from the National University of Singapore, Sadie possesses a solid academic foundation. Her expertise lies in Python programming, machine learning, and deep learning, along with skills in mathematical modeling and computational image analysis. Currently working as a research assistant at the University of Western Australia, Sadie contributes to developing AI solutions for analyzing 3D CT images. Alongside her research role, she teaches part-time at the Engineering Institute of Technology (EIT). With previous experience in data analysis, IT consultancy, and research, Sadie brings a wealth of knowledge to her work, marked by her problem-solving abilities, effective communication, and collaborative spirit.