The problem of defining and classifying power system stability has been addressed by several previous CIGRE and IEEE Task Force reports. These earlier efforts, however, do not completely reflect current industry needs, experiences and understanding. In particular, the definitions are not precise and the classifications do not encompass all practical instability scenarios.This report developed by a Task Force, set up jointly by the CIGRE Study Committee 38 and the IEEE Power System Dynamic Performance Committee, addresses the issue of stability definition and classification in power systems from a fundamental viewpoint and closely examines the practical ramifications. The report aims to define power system stability more precisely, provide a systematic basis for its classification, and discuss linkages to related issues such as power system reliability and security.

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This paper investigates the use of reactive power reserves (RPR) as an indicator to estimate voltage stability margin (VSM) in an online environment. The methodology relies upon the relationship between system-wide RPRs and VSM. Statistical multilinear regression models (MLRM) are utilized in order to express how variations in RPRs can be transformed into direct information about VSM. Data regarding RPRs and system VSM are obtained through an offline voltage stability assessment (VSA) and stored in a database for further MLRM development. Different load increase directions and a comprehensive list of contingencies are considered to account for uncertainty present in real-time operations. Once properly designed and validated, the MLRMs are ready to be used in the online environment. The methodology is tested on the IEEE 30-bus system and a real size test system containing 1648 buses. Preliminary results show that MLRMs can be successfully employed in online VSM estimation.

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In this paper, we propose linear operator theoretic framework involving Koopman operator for the data-driven identification of power system dynamics. We explicitly account for noise in the time series measurement data and propose robust approach for data-driven approximation of Koopman operator for the identification of nonlinear power system dynamics. The identified model is used for the prediction of state trajectories in the power system. The application of the framework is illustrated using an IEEE nine bus test system.