IEEE SmartGridComm Tutorial 2 - Power System Machine Learning Applications
Channel: Luigi Vanfretti
Date: 2020-10-26

IEEE SmartGridComm 2020, Tutorial 2: 
https://sgc2020.ieee-smartgridcomm.org/program/tutorials

Title: Power System Machine Learning Applications - From Physics-Informed Learning for Decision Support to Inference at the Edge for Control

Presenters: Luigi Vanfretti, Tetiana Bogodorova and Sergio A. Dorado-Rojas

Github Repository:
https://github.com/ALSETLab/Tutorial_SGC_2020

Abstract:
Electrical power networks are facing unique challenges in their operation and control. With the increasing penetration of variable & intermittent renewable energy sources and limited transmission capabilities, grid operations and control is becoming evermore complex. However, the transition into the “digital utility” is brining unprecedented opportunities to leverage measurement data with conventional analysis methods, that when combined together, can help in achieving the goals for a “green” energy transition. In this context, Artificial Intelligence and Machine Learning are emerging as a cohort of theory, methods and technologies that if applied properly to solve power system problems, may have an invaluable contributions to solve existing and future grid challenges.

This tutorial provides insights from a team of power system specialists on the development of Machine Learning-based for power system applications using both measurements and physics-informed simulation. The scope of the presentation is on how to frame to power system problems and apply ML existing methods and technologies, and not on ML itself.

The tutorial is divided in three parts. First, an overview on today’s hierarchical power system operation and control is given, identifying a few of the potential areas where ML can be be of substantial benefit to power system operations for decision making at the control center to inference at edge devices in control/protection.

Second, on-going research in the development of a “recommender system” for operator decision support will be presented. Such type of predictive application cannot rely on measurement data alone, it has to be complemented with physics-informed models and simulation. In other words, this is a case where both measurement-based and simulation-based ML analytics need to be combined. Hence, this part of the presentation makes a strong emphasis on the careful design of simulation models, algorithms for automated simulation scenario design and software pipelines for automated generation of simulation data. Many challenges were faced when building a toolchain that makes this possible. We illustrate the challenges faced to adopt not only ML methods, but the computing software environments and hardware required in ML workflows so to be able to scale for realistic use cases.

Finally, we illustrate the first results obtained using our proposed approach for classification of power system stability using both traditional data science methods and Deep Learning. Finally, on-going research in the development of “edge” applications in power systems will be presented. The use case is the detection of undesirable sub-synchronous control interactions between the power grid and wind turbines for potential use in control and protection at the “edge”, i.e at the wind-farm location, which would require a ML-based apparatus capable to provide reliable predictions in real-time. We illustrate the challenge of having a reduced measurement data set to train such detection algorithm, and how simulation helps to solve this problem. Furthermore, we illustrate the performance of the developed ML-based solution on three different hardware platforms.