Power Flow Studies using Principal Component Analysis
This paper employs Principal Component Analysis (PCA), a data mining technique, to study power flow. A simulation-based process is established to perform the study, which consists of steps such as system linearization, training data construction, PCA analysis and results interpretation. The PCA results are presented in a straightforward manner, and interpreted from power system perspective. The conclusions not only are consistent with the well-known facts such as PQ decoupling, but also discover hidden facts such as correlation pattern among input variables and state variables. The proposed power flow study method is not only a helpful tool for power system operators in practice, but also beneficial for engineering students in study.
R. Bo and F. Li, "Power Flow Studies using Principal Component Analysis," Proceedings of the 40th North American Power Symposium (2008, Calgary, Canada), pp. 1-6, Institute of Electrical and Electronics Engineers (IEEE), Sep 2008.
The definitive version is available at https://doi.org/10.1109/NAPS.2008.5307323
40th North American Power Symposium, NAPS2008 (2008: Sept. 28-30, Calgary, Canada)
Electrical and Computer Engineering
Keywords and Phrases
Correlation Patterns; Data Mining Techniques; Input Variables; PCA Analysis; Power Flow Studies; Power Flows; Power System Operators; Power Systems; PQ Decoupling; Simulation-Based; State Variables; Training Data; Data Flow Analysis; Data Mining; Electric Power Transmission Networks; Principal Component Analysis; Power Flow; Principal Component Analysis (PCA)
International Standard Book Number (ISBN)
Article - Conference proceedings
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