Machine Learning-Based Multiclass Anomaly Detection and Classification in Hybrid Active Distribution Networks-Reference-Cited by-同舟云学术

Machine Learning-Based Multiclass Anomaly Detection and Classification in Hybrid Active Distribution Networks

Published:2024 Issue: Volume:12 Page:120131-120141
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Chandio Sadullah¹^ORCID,Laghari Javed Ahmed¹^ORCID,Bhayo Muhammad Akram¹^ORCID,Koondhar Mohsin Ali¹^ORCID,Kim Yun-Su²^ORCID,Graba Besma Bechir³,Touti Ezzeddine⁴^ORCID

Affiliation:

1. Department of Electrical Engineering, Science and Technology, Quaid-e-Awam University of Engineering, Nawabshah, Sindh, Pakistan

2. Graduate School of Energy Convergence, Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea

3. Department of Physics, College of Science, Northern Border University, Arar, Saudi Arabia

4. Department of Electrical Engineering, College of Engineering, Northern Border University, Arar, Saudi Arabia

Funder

Gwangju Institute of Science and Technology (GIST) Research Project

GIST in 2024. Also the authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10638046.pdf?arnumber=10638046

Reference38 articles.

1. A Mixed Integer Linear Programming Based Load Shedding Technique for Improving the Sustainability of Islanded Distribution Systems

2. Artificial neural network based islanding detection technique for mini hydro type distributed generation

3. Real-Time AI-Based Anomaly Detection and Classification in Power Electronics Dominated Grids

4. Faults detection and diagnosis of PV systems based on machine learning approach using random forest classifier

5. End to end machine learning for fault detection and classification in power transmission lines