Online Identification of Cascading Events in Power Systems With Renewable Generation Using Measurement Data and Machine Learning-Reference-Cited by-同舟云学术

Online Identification of Cascading Events in Power Systems With Renewable Generation Using Measurement Data and Machine Learning

Published:2023 Issue: Volume:11 Page:72343-72356
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Nakas Georgios A.¹^ORCID,Dirik Alara²,Papadopoulos Panagiotis N.¹^ORCID,Matavalam Amarsagar Reddy Ramapuram³^ORCID,Paul Oliver⁴,Tzelepis Dimitrios¹^ORCID

Affiliation:

1. Department of Electronics and Electrical Engineering, University of Strathclyde, Glasgow, U.K.

2. Department of Computer Engineering, Boğaziçi University, Istanbul, Turkey

3. School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA

4. CheckSolar, Düsseldorf, Germany

Funder

Engineering and Physical Sciences Research Council (EPSRC) Research Excellence Award

Supergen Energy Networks Hub

U.K. Research and Innovation (UKRI) Future Leaders Fellowship

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10179902.pdf?arnumber=10179902

Reference41 articles.

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4. Real-time multi-state classification of short-term voltage stability based on multivariate time series machine learning

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