Machine Learning in Power System Operations: Training Data

Conference · Mon Jul 17 04:00:00 EDT 2023

OSTI ID:2007447

Bukowski, Stephen Arthur ^[1]; Sticht, Christopher Roger ^[1]

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1. Idaho National Laboratory

Reliability and stability of the electric grid today has depended upon operations of the grid which include the protective relay. Today, the electricity sector faces new challenges with the shift of generation resource characteristics away from the traditional “big iron” generation to inverter-based resources (IBR) which shift the physics and assumption used in grid operation and protection. These changing conditions represent new challenges for protective relays (identification of faults) and increased challenges for protection engineers (correct settings and configuration, reduction of mis-operations), both issues recognized in research and industry. Finding new approaches to reduce mis-operations in relaying and new approaches to fault identification is critical to grid operations. Using today’s modern technology of embedded systems, edge computing, machine learning (ML), and communications we can help address challenges and augment and improve on existing power system operations methodologies.

• Research Organization: Idaho National Laboratory (INL), Idaho Falls, ID (United States)
• Sponsoring Organization: 64
• DOE Contract Number: AC07-05ID14517
• OSTI ID: 2007447
• Report Number(s): INL/CON-23-72799-Rev000
• Country of Publication: United States
• Language: English

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
Fault Identification
Labeled Datasets
Machine Learning
Power Systems
Protection & Control