Multi-Source Data Aggregation and Real-time Anomaly Classification and Localization in Power Distribution Systems-Reference-Cited by-同舟云学术

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Multi-Source Data Aggregation and Real-time Anomaly Classification and Localization in Power Distribution Systems

Published:2023 Issue: Volume: Page:1-1
ISSN:1949-3053
Container-title:IEEE Transactions on Smart Grid
language:
Short-container-title:IEEE Trans. Smart Grid

Author:

Ganjkhani Mehdi¹^ORCID,Gholami Amir²^ORCID,Giraldo Jairo¹^ORCID,Srivastava Anurag K.³^ORCID,Parvania Masood¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, USA

2. ETAP (part of Schneider Electric), Irvine, CA, USA

3. Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA

Funder

Solar Energy Technologies Office

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/5165411/5446437/10253676.pdf?arnumber=10253676

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Fault-MTL: A Multi-task Deep Learning Approach for Simultaneous Fault Classification and Localization in Power Systems;Journal of Control, Automation and Electrical Systems;2024-08-11

2. A Review of Power System False Data Attack Detection Technology Based on Big Data;Information;2024-07-28

3. d-SyncAED: Distribution Synchrophasor Anomaly and Event Detection Tool in Real-Time;2024 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA);2024-05-21

4. A method for assessing and localizing protection measurement loop errors based on an improved similarity algorithm;2024-03-20

5. Machine Learning-Based Multiclass Anomaly Detection and Classification in Hybrid Active Distribution Networks;IEEE Access;2024

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