Webinar

Using AI for Power System Fault Detection and Classification

Learn how engineers can model the IEEE 123 Node Test Feeder model in a simulation environment to perform fault finding that may occur and fault classification of the model.

August 13, 2026
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11:00 - 12:00
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Overview

Learn how engineers can model the IEEE 123 Node Test Feeder model in a simulation environment to perform fault finding that may occur and fault classification of the model. This is done by running multiple simulations with fault sequences injected into the power system modelled in Simulink. We will go through how to generate synthetic data by running simulations of the power system, how to extract the data, and put together a dataset which will be used to train a Predictive Maintenance model meant to predict where the fault occurred in the power system and the specifics of the type of fault that occurred.

Key Highlights of the Webinar

  • Create an IEEE bus system using Simscape Electrical.
  • Inject faults into a bus system and view results.
  • Build Predictive Maintenance models from fault data using either the Classification Learner App in MATLAB or programmatically.
  • Learn how to properly choose the best model based on validation accuracies, test scores, and other factors.

Who Should Attend

  • Data Scientists
  • Machine Learning Engineers
  • Reliability& Maintenance Engineers
  • Any decision makers looking to understand how equipment data can be used to drive business impact

Speaker Bio

Nur'ain Areff is a Software Consultant with a background in Biomedical and Electrical Engineering from the University of the Witwatersrand. Nur'ain specialises in Model-Based Design and integrating these designs with hardware, as well as incorporating machine learning and automation to existing workflows. She has been involved in many projects including developing low-cost versions of equipment, computer vision systems for safety and delivery of technical workshops.

Enzo du Plessis is a Model-Based Design Consultant a t Optinum. With a background in Mechanical Engineering, he primarily helps clients model and simulate physical systems using Simulink and Simscape. With a keen interest in AI/Data Science, he leverages the best of both AI and simulation engineering to help clients make the best decisions for their businesses.

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