Year: 2025
Type: article
Abstract: The modernization of electrical power systems through smart grid technologies has introduced unprecedented opportunities for enhanced efficiency, reliability, and sustainability. However, this digital transformation has also expanded the attack surface for cyber threats, making critical infrastructure increasingly vulnerable to sophisticated cyberattacks. This paper examines the application of artificial intelligence (AI) and machine learning (ML) technologies for threat detection in smart grid systems within the United States context. Through a comprehensive analysis of current deployment scenarios, threat landscapes, and AI-driven security frameworks, this study demonstrates how intelligent systems can enhance the resilience of critical infrastructure. The research presents empirical data from major U.S. utilities, evaluates the effectiveness of various AI algorithms in detecting anomalous behavior, and provides recommendations for implementing robust AI-based security solutions in smart grid environments.
Authors: Esther Chinwe Eze, Grace A. Durotolu, Fen Danjuma John, Shakirat O. Raji
Institutions: University of North Texas, Troy University, Davenport University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412471763