TRAINING.

Overview

After participating in this course, you will have an understanding of:

• The basics of machine learning algorithms,
• The overall implementation of machine learning from collecting the data to the classification process,
• Sources of machine learning errors,
• New trends in machine learning
• The use of available machine learning software,
• Application of machine learning to different power engineering problems,

Description

The application of machine learning in the power system is an integral part of the smart grid. This course introduces the basic knowledge of machine learning for power engineers with an emphasis on applications related to the power industry. Types of machine learning algorithms like supervised, unsupervised and deep learning will be discussed. Also, the practical problems that diminish the efficiency of machine learning like overfitting and imbalance of data will be highlighted. The main focus of the course is on the practical application of machine learning to the power grid without going deep into the math or the statistics behind the different algorithms. Hands-on tutorials using free software will be utilized to demonstrate the application of machine learning to the power industry.

Course Outline

• Introduction
• Examples of machine learning applications in power engineering
• Problems with machine learning and their remedies
• New trends in machine learning
• Introduction to FREE commercial machine learning software
• Hands-on study cases (using WEKA)

Who Should Attend

This course is intended as an introduction to machine learning and its application to the power industry for power managers and engineers who are dealing with different parts of the power grid. The course does not require any background in programming, math, or statistics.

Please note: You can check other time zones here.

Syllabus

Day I (Theory)

Introduction

• Type of machine learning algorithms
  • Supervised learning
  • Unsupervised learning
  • Enhanced learning
  • Deep learning
• The overall machine-learning process
  • Data collection
  • Extracting features
  • Learning process
  • Application of machine learning algorithms

Examples of Machine Learning Applications in Power Engineering

• Predicting the output of renewable energy sources
• Identifying the source of partial discharge in electrical equipment
• Estimating the transformer health index
• Detecting the source of transient in the power grid
• Enhancing the operation of the distribution system using data from smart meters
• Quantifying the damage in outdoor ceramic and non-ceramic insulators

Problems With Machine Learning and Their Remedies

• Overfitting
• Data imbalance
• Curse of dimensionality

New Trends in Machine Learning

• Application of multiple sensors in power asset monitoring
• Big data
• Drone application in power asset inspection

Day II (Application)

Introduction To FREE Commercial Machine Learning Software

• Hands-on tutorial on using WEKA to explore the following:
  • Application of supervised learning
    • Regression
    • Classification
  • Application of unsupervised learning
    • Clustering

Hands-on Study Cases (Using WEKA):

• Detecting different defects in outdoor ceramic insulators:
  • Problems with ceramic insulators: crack, pollution discharge, etc.
  • Sensor application and data gathering
  • Classifying the different insulator defects.
• Predicting transformer oil parameters:
  • Importance and types of transformer oil tests
  • Predicting transformer oil interfacial tension and furan content.

Instructor

Ayman El-Hag, PhD, P. Eng., SMIEEE

Ayman El-Hag, PhD, SM IEEE, P. Eng., received his Ph.D. degree from the University of Waterloo in 2003. He joined the Saudi Transformer Co. as a Quality Control and testing Engineer from 1993 to 1999. His main duties included the implementation of ISO 9001 provisions and the testing of distribution transformers as per IEC 60076. Currently, Dr. El-Hag is an adjunct professor and lecturer at the University of Waterloo. Dr. El-Hag main area of interest is condition monitoring and diagnostics of electrical insulation.

Dr. El-Hag received several funds from NSERC and Qatar foundation that are mainly related to inspection of outdoor ceramic insulators, transformers and design of non-ceramic insulators. The total value of the funds is more than two million Canadian dollars. He published many referred journal and conference papers in the area of monitoring of outdoor insulators and transformers. Dr. El-Hag is a registered engineer in the province of Ontario, a senior member of IEEE, a guest editor for energies special issue “High Voltage Engineering and Application”, was an associate editor for the IEEE Dielectric and Electrical Insulation Transaction (2018-2019) and the middle east editor for the IEEE Insulation magazine (2016-2018). Also, Dr. El-Hag is a member of the IEEE outdoor insulation committee and the IEEE Std 1523 (IEEE Guide for the Application, Maintenance, and Evaluation of Room Temperature Vulcanizing (RTV) Silicone Rubber Coatings for Outdoor Insulation Applications).