Journal
IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 33, Issue 1, Pages 45-54Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2017.2688446
Keywords
Clustering; data analytics; decision trees; phasor measurement units; probabilistic transient stability; renewable generation
Categories
Funding
- EPSRC-India project ACCEPT [EP/K036173/1]
- EPSRC project Autonomic Power Systems [EP/I031650/1]
- Engineering and Physical Sciences Research Council [EP/K036173/1, EP/N030028/1, EP/I031650/1] Funding Source: researchfish
- EPSRC [EP/K036173/1, EP/N030028/1, EP/I031650/1] Funding Source: UKRI
Ask authors/readers for more resources
The paper introduces a probabilistic framework for online identification of post fault dynamic behavior of power systems with renewable generation. The framework is based on decision trees and hierarchical clustering and incorporates uncertainties associated with network operating conditions, topology changes, faults, and renewable generation. In addition to identifying unstable generator groups, the developed clustering methodology also facilitates identification of the sequence in which the groups lose synchronism. The framework is illustrated on a modified version of the IEEE 68 bus test network incorporating significant portion of renewable generation.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available