IEEE Electrification - December 2022 - 90

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Successful Applications and
Future Challenges of Machine Learning
for Power Systems
A Summary of Recent Activities by the IEEE WG
on Machine Learning for Power Systems
By Fangxing Li
M
ACHINE LEARNING (ML),
or artificial intelligence
(AI), is one of the emerging
technologies for implementing the
next-generation smart grid. In
recent years, significant efforts have
been devoted to exploring the
potentials of ML and AI for solving
complex power system problems.
The IEEE WG on Machine Learning
for Power Systems has recently
organized several events in this
heatedly studied area. These events
include the following:
x the 2021 IEEE Workshop on
Machine Learning for Power Systems,
held virtually on 17 November
2021 [information is available
at the IEEE Power & Energy Society
(PES) Resource Center at
https://resourcecenter.ieee-pes.
org/technical-committees/videos/
PES_PSOPE_WEB_111721.html]
x the panel session " Machine Learning
Techniques for Power System
Decision-Making " on 19 July 2022
at the PES General Meeting (GM)
x the supersession " Artificial Intelligence
in Power Systems " on 20
July 2022 at the PES GM.
Digital Object Identifier 10.1109/MELE.2022.3211126
Date of current version: 24 November 2022
90
A total of 19 presentations were
given at these three events. Detailed
information is provided in the following
sections.
2021 IEEE Workshop on
Machine Learning for
Power Systems, held on
17 November 2021
x " From AlphaGo to Power System
AI: Vision and Case Studies, " by
Fran Li and Jin Zhao, University
of Tennessee, USA
x " Physics-Informed Deep Reinforcement
Learning for Power
System Optimization and Control, "
by Junbo Zhao, Mississippi
State University, USA
x " Application of Machine Learning
to Improve Grid Operation Reliability, "
by Pengwei Du, ERCOT, USA
x " Neural Network Applications for
Power Systems: Is It Time to Trust
Them? " by Spyros Chatzivasileiadis,
Technical University of Denmark,
Denmark
x " Rotor Angle Stability Prediction
of Power Systems With High
Wind Power Penetration, " by C.Y.
Chung, University of Saskatchewan,
Canada
x " Physics-Inspired ML for Cyberphysical
Event Detection,
IEEE Electrification Magazine / DECEMBER 2022
Classification, and Root Cause
Analytics, " by Anurag K. Srivastava,
West Virginia University,
USA.
Panel Session " Machine
Learning Techniques for Power
System Decision-Making " at
the PES GM 2022
x " Scalable Reinforcement Learning
for Wide-Area Oscillation
Control, " by Aranya Chakrabortty
and Sayak Mukherjee, North Carolina
State University, USA
x " Using Reinforcement Learning
to Increase Grid Security Under
Contingency Conditions, " by Ross
Guttromson, Stephen Verzi, and
Asael Sorensen, Sandia National
Laboratories, USA
x " Application and Demonstration
of Learning-Based Volt-Var Control
at Distribution Grid With
Renewables, " Di Shi, AInergy
LLC, USA
x " A Communication-Free Fault
Detection Method Using Long
Short-Term Memory for a
Closed-Loop Distribution System
Protective Relay, " by Yun-Su Kim
and Sung-Ryeol Han, Gwangju
Institute of Science and Technology,
South Korea

https://orcid.org/0000-0003-1060-7618 https://resourcecenter.ieee-pes.org/technical-committees/videos/PES_PSOPE_WEB_111721.html https://resourcecenter.ieee-pes.org/technical-committees/videos/PES_PSOPE_WEB_111721.html https://resourcecenter.ieee-pes.org/technical-committees/videos/PES_PSOPE_WEB_111721.html

IEEE Electrification - December 2022

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