Distributed Adaptive Learning Framework for Wide Area Monitoring of Power Systems Integrated with Distributed Generations

ISSN Print: 1949-243X
ISSN Online: 1947-3818
www.scirp.net/journal/epe
E-mail: [email protected]

Citations

"Distributed Adaptive Learning Framework for Wide Area Monitoring of Power Systems Integrated with Distributed Generations"
written by Kang Li, Yuanjun Guo, David Laverty, Haibo He, Minrui Fei,
published by Energy and Power Engineering, Vol.5 No.4B, 2013

has been cited by the following article(s):

[1]	Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges
	2021

[2]	Detection of Transient Signals in Smart Grid Using Artificial Neural Network Modeling and JAYA Optimization
	2018

[3]	Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study
	Complexity, 2018

[4]	A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring
	Electric Power Systems Research, 2015

[5]	Loss-of-Main Monitoring and Detection for Distributed Generations Using Dynamic Principal Component Analysis
	Journal of Power and Energy Engineering, 2014

[1]	Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges Energies, 2021 DOI:10.3390/en14123654

[2]	Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study Complexity, 2018 DOI:10.1155/2018/8496187

[3]	Detection of Transient Signals in Smart Grid Using Artificial Neural Network Modeling and JAYA Optimization 2018 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia), 2018 DOI:10.1109/ISGT-Asia.2018.8467819

[4]	A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring Electric Power Systems Research, 2015 DOI:10.1016/j.epsr.2015.06.002

[5]	Loss-of-Main Monitoring and Detection for Distributed Generations Using Dynamic Principal Component Analysis Journal of Power and Energy Engineering, 2014 DOI:10.4236/jpee.2014.24057