Smart Power Systems Transformation: Advanced Fault Detection With Edge Computing and Signal Processing in LV Networks With EV Charging Stations
This research presents a novel framework for improving fault detection and grid resilience in modern power systems by leveraging edge computing, optimized infrastructure placement, and advanced signal processing. At the core of the approach is an innovative time-frequency analysis method that enhanc...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11071546/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This research presents a novel framework for improving fault detection and grid resilience in modern power systems by leveraging edge computing, optimized infrastructure placement, and advanced signal processing. At the core of the approach is an innovative time-frequency analysis method that enhances fault discrimination, even in noisy environments. By strategically positioning smart meters and Electric Vehicle (EV) charging stations, the framework improves fault detection efficiency and overall system reliability. The Adaptive SBCT index dynamically fine-tunes fault identification, ensuring a more responsive power grid. Additionally, Kernel Principal Component Analysis (KPCA) streamlines data processing without compromising critical information, enhancing real-time performance. Extensive simulations and case studies validate the framework’s effectiveness across diverse low-voltage networks, demonstrating its potential to minimize power outages, reduce maintenance costs, and strengthen grid reliability. Future directions include large-scale real-world deployment and integration with renewable energy sources to further enhance system sustainability and scalability. |
|---|---|
| ISSN: | 2169-3536 |