Asset Health Monitoring and Predictive Maintenance of Electric Utility Equipment using Artificial Intelligence

Date Time
28 Oct 2020
10:00 PM EDT

Overview

The use of AI techniques on time-series data is growing in popularity across electric utilities sector for asset management, demand response, outage management, customer services, energy storage, renewable resources, and many other areas in the power generation and delivery system.  

In this webinar, we will present a case study on “Identifying Risk in Underground Utility Cable Systems using Machine Learning and Deep Learning”. Predictive maintenance begins with understanding how cable system failures occur. Analyzing and interpreting results from partial discharge (PD) measurements taken in the field can be a complex task for humans. Machine learning algorithms and deep learning algorithms are used to automatically identify and categorize markers of defects contained in the PD measurements. These algorithms are used to categorize different defect types by risk of going to failures soon. Differentiating cables with “high to low risk defects” along with those that are “defect free” enables predictive maintenance. Examples of identified defects will be presented.

You will also learn how to apply AI using MATLAB® for asset condition monitoring, and find out about tools and fundamental approaches for developing advanced predictive models on time series data. Using a real-world faulty dataset we will show two approaches of building deep learning models using convolution neural networks and recurrent neural networks, and finally deploying the models on an edge devices or the cloud.

Specific Topics include:

  • Acquiring, creating and annotating faulty datasets
  • Applying time-frequency transformations, extracting established signal features, and automating deep feature extraction using Invariant Scattering Convolutional Networks
  • Building and comparing Deep Learning models with CNNs and LSTMs
  • Generating stand-alone CUDA code to deploy models on edge devices
  • Deploying and integration on the cloud

About the Presenter

1.      Shishir Shekhar

With over a decade of engineering and management experience in the Power & Energy industry, Shishir Shekhar is responsible for Worldwide Utilities & Energy business segment at MathWorks Inc. Shishir leads the global Market & Strategy functions supporting the breadth of the MathWorks product line from Artificial Intelligence, Advanced Simulations, Cloud Computing, IoT, Big Data, etc. Shishir advises leading Electric Utilities, Renewable Energy and Power Systems Automation organizations globally on adopting and implementing Digital Technologies and Solutions for their Grid Modernization and Digitization Initiatives.

Before joining MathWorks Inc, Shishir was Engineering Lead, New Initiatives (Grid Modernization and Digitization) at National Grid USA, where he led innovation programs and pilot demonstration projects on developing Digital Solutions for Asset Management, Grid Analytics, etc. Shishir also led large, multimillion-dollar Infrastructure projects such as Integration of Wind, Solar and Energy Storage technologies on Electric T&D systems, Planning and Commissioning of High Voltage Overhead and Underground Transmission Networks.

Shishir is a Senior Member of IEEE and member of CIGRE.

Shishir holds a Master’s Degree in Business Management from Harvard University, USA and a Master’s Degree in Electrical Power Engineering from Northeastern University, USA. Shishir was a Research Associate at Massachusetts Institute of Technology (MIT) USA, where his research focus was in the area of Smart Grids, studying Economics of Energy Storage and Wind Technologies for Improving Grid Reliability and identifying the use of storage for new revenue streams in energy and power markets across USA. Shishir received his Bachelor’s Degree in Electronics and Communication Engineering from SRM University, India and was awarded Notable Alumni of the university in 2019.

2.      Akhilesh Mishra

Akhilesh Mishra is an Application Engineer for the Electric Utility and Healthcare Industry at MathWorks. He specializes in the signal/data processing, artificial intelligence and GPU computing workflows.  He has been with MathWorks since 2016. Akhilesh holds a M.S. degree from University of Kansas where he was the signal processing lead in a group working on radar and sonar systems for sounding the ice sheets of Greenland and Antarctica to study global sea-level rise.

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