Fault Detection and Diagnostics in Power Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".
Deadline for manuscript submissions: 20 November 2025 | Viewed by 615
Special Issue Editors
Interests: fault detection; diagnosis; condition monitoring; mechatronics; instrumentation; control; digital systems
Interests: model-based and data-driven fault detection; diagnostics; prognosis; multi-agent Systems (satellites, drones, vehicles, etc.); machine learning; intelligent systems; intelligent manufacturing; Industry 5.0; systems and control theory; linear and nonlinear controller/observer design; avionics; sensors; measurement
Special Issue Information
Dear Colleagues,
Electrical power systems are critical not only for industry but also for daily human life, as they are responsible for the generation, transmission, and distribution of energy across the globe.
Faults can occur at any stage of a power system—generation, transmission, or distribution—and any of these can directly impact end users.
Continuous efforts have been made to address these issues. However, the growing energy demand and the increasing integration of renewable energy sources present significant challenges for maintaining power system stability and reliability.
This Special Issue is dedicated to the publication of novel strategies and methodologies for the detection, classification, and diagnosis of faults in power systems. It welcomes contributions that involve conventional approaches as well as those based on emerging technologies such as artificial intelligence, machine learning, and data-driven analytics.
The aim is to provide a comprehensive platform for researchers and professionals to share innovative solutions that enhance the resilience, safety, and efficiency of modern power systems.
Dr. Jesus Rivera-Guillen
Dr. Afshin Rahimi
Guest Editors
Manuscript Submission Information
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Keywords
- fault detection
- fault diagnosis
- fault classification
- power system faults
- power system reliability
- power system stability
- condition monitoring
- smart grids
- power transmission
- power distribution
- power generation
- intelligent fault analysis
- artificial intelligence in power systems
- machine learning for fault detection
- data-driven diagnostics
- predictive maintenance
- protection systems
- grid resilience
- renewable energy integration
- anomaly detection in power systems
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