Battelle Memorial Institute

Yousu Chen

Pak Chung Wong

Kevin P Schneider

Jeffery E Dagle

Patrick S Mackey

Marcelo A Elizondo

Nader A Samaan

George Chin, Jr

Xinya Li

Bharat Vyakaranam

Mallikarjuna Vallem

Qiuhua Huang

Kirsten L Pierce

Dimitri V Zarzhitsky

Zhenyu Huang

Jian Ma

Pavel V Etingov

Blaine J McGary

The present disclosure describes systems and techniques that enhance effectiveness and efficiency of a contingency analysis tool that is used for studying the magnitude and likelihood of extreme contingencies and potential cascading events across a power system. The described systems and techniques include deploying the contingency analysis tool in a high-performance computing (HPC) environment and incorporating visual situational awareness approaches to allow power system engineers to quickly and efficiently evaluate multiple power system simulation models. Furthermore, the described systems and techniques include the power system contingency-analysis tool calculating and coordinating protection element settings, as well as assessing controls of the power system using small-signal nomograms, allowing power system engineers to more effectively comprehend, evaluate, and analyze causes and effects of cascading events against a topology of a power system.

Enhanced dynamic contingency analysis for power systems

Methods and systems for automated decision support in analyzing operation data from a complex network. Embodiments of the present invention utilize these algorithms and techniques not only to characterize the past and present condition of a complex network, but also to predict future conditions to help operators anticipate deteriorating and/or problem situations. In particular, embodiments of the present invention characterize network conditions from operation data using a state estimator. Contingency scenarios can then be generated based on those network conditions. For at least a portion of all of the contingency scenarios, risk indices are determined that describe the potential impact of each of those scenarios. Contingency scenarios with risk indices are presented visually as graphical representations in the context of a visual representation of the complex network. Analysis of the historical risk indices based on the graphical representations can then provide trends that allow for prediction of future network conditions.

Decision support systems and methods for complex networks

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Richland, WA, United States of America

Yousu Chen