Decision tree-based classification of multiple operating conditions for power system voltage stability assessment

Luigi Vanfretti, V. S.Narasimham Arava

Research output: Contribution to journalArticlepeer-review

49 Scopus citations

Abstract

This paper presents a method that performs classification of thousands of operating conditions w.r.t. power system voltage stability by using decision trees. The proposed method uses a new and flexible classification criterion that allows to identify operating conditions that are near or within the region for which the system is voltage unstable, and more importantly, that can consider operational requirements. The method creates both training and test data sets when building and validating the decision trees. To minimize computational burden, a sampling method is proposed, this method exploits the Saddle Node Bifurcation conditions to explore the operational space used to train the decision trees. Case studies were performed using the IEEE 9-bus system for several operating conditions and different network configurations. This paper also proposes the use of time domain simulations to assess the prediction accuracy of decision trees. Decision trees were created for network configurations involving outage of the line were tested on test sets and also using time domain simulations results from PSS/E. The ability to classify the degree of voltage stability of a multitude of operation conditions could be useful to aid operators in selecting and applying preventive measures to steer away the system from unstable conditions or conditions that are close to breaching operational requirements w.r.t. voltage stability.
Original languageEnglish (US)
Pages (from-to)106251
JournalInternational Journal of Electrical Power and Energy Systems
Volume123
DOIs
StatePublished - Jun 25 2020
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-14
Acknowledgements: This work was supported in part by the European Union Seventh Framework Programme (FP7/2007-2013) under Grant 283012, within Innovative Tools for Electrical System Security within Large Areas (iTESLA), in part by the New York State Energy Research and Development Authority (NYSERDA) through the Electric Power Transmission and Distribution (EPTD) High Performing Grid Program under agreement number 137951, in part by the Engineering Research Center Program of the National Science Foundation and the Department of Energy under Award EEC-1041877 and in part by the CURENT Industry Partnership Program, and in part by the Center of Excellence for NEOM Research at King Abdullah University of Science and Technology.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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