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Title: Real-time deployment of a novel synchrophasor based voltage stability assessment algorithm
Authors: Adewole, Adeyemi Charles 
Tzoneva, Raynitchka 
Keywords: CART;Decision Trees;Machine Learning;Phasor Measurement Units;Synchrophasors;Voltage Stability Assessment;Wide Area Monitoring System
Issue Date: 2014
Publisher: Praise Worthy Prize
Source: International Review of Electrical Engineering, 9(5):1021-1033, 2014
Abstract: This paper proposes a Real-Time Voltage Stability Assessment (RVSA) algorithm based on Classification and Regression Trees (CARTs) for the prediction of the state of the power system and the system’s margin to voltage collapse. A novel RVSA index based on the Effective Generator Reactive Power Reserve (EGRPR) using the field current from synchronous generators is used by the proposed RVSA algorithm. Wide area synchrophasor measurements obtained from Phasor Measurement Units (PMUs) using various scenarios involving the long-term voltage stability dynamics of transformer Under-Load Tap Changers (ULTCs), generator Over-Excitation Limiters (OXLs), and credible contingencies are used in creating the knowledge base for training the CARTs. The trained CARTs are afterwards deployed online in a testbed incorporating a Programmable Logic Controller (PLC) for real-time assessment/prediction. The performance of the proposed algorithm is tested and validated on an equivalent 10-bus multi-machine network using the Real Time Digital Simulator® (RTDS) in a ‘hardware-in-the loop’ architecture with the PLC. Comparisons and analyses made from the results obtained verify the simplicity and accuracy of the proposed RVSA index and algorithm. Their effectiveness for various scenarios such as load variation, topology change, ULTC and OXL actions, are illustrated.
Appears in Collections:Eng - Journal articles (not DHET subsidised)

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