Decision Tree-Based Online Voltage Security Assessment Using PMU Measurements

By R. Diao, Kai Sun, V. Vittal, R. O’Keefe, M. Richardson, N. Bhatt, D. Stradford, S. Sarawgi. 2009

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Giuseppe Ateniese
2. Luigi Vincenzo Mancini
3. Angelo Spognardi
4. Antonio Villani
5. Domenico Vitali
6. H. Shayanfar
7. Adnan M. Abu-Mahfouz
8. M. Esmaili

Citations in the corpus

• Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers (2013, background): “ghts. 1 Introduction Machine learning classifiers are designed to make effective and efficient prediction of “patterns” from large data sets. Many applications have been proposed in the literature (e.g., [27, 54, 49, 23, 25]) and machine learning algorithms pervade several contexts of information techno”
• Redundant Observability PMU Placement in the Presence of Flow Measurements Considering Contingencies (2013, background): “measurements across a wide-area power system because the data are not time-synchronized [2].”
• A Review of Machine Learning Approaches to Power System Security and Stability (2020, methodology): “Diao et al. [123] pre-trained a DT algorithm offline using a voltage security analysis conducted using the past representative and forecasted daily operating conditions that involves 29 different scenarios for an American Electric Power (AEP) test system.”