Data-mining-based approach for predicting the power system post-contingency dynamic vulnerability status

Abstract

Summary This paper proposes a data‐mining‐based approach for predicting the power system post‐contingency vulnerability status in real time. To this aim, the system dynamic vulnerability regions (DVRs) are first determined by applying singular value decomposition, by means of empirical orthogonal functions (EOFs), to a post‐contingency data base obtained from phasor measurement units (PMUs) adequately located throughout the system. In this way, the obtained pattern vectors (EOF scores) allow mapping the DVRs within the coordinate system formed by the set of EOFs, which permits revealing the main patterns immersed in the collected PMU signals. The database along with the DVRs enable the definition, training, and identification of a support vector classifier (SVC) which is employed to predict the post‐contingency vulnerability status as regards three short‐term stability phenomena, that is: transient stability, short‐term voltage stability, and short‐term frequency stability (TVFS). Enhanced procedures for feature extraction and selection as well as heuristic optimization‐based parameter identification are proposed to ensure a robust performance of the SVC. Numerical results, obtained by implementation of the proposed approach on two different size test power systems, demonstrate the methodology viewpoint and effectiveness. Copyright © 2014 John Wiley & Sons, Ltd.

Post-disturbance transient stability assessment of power systems towards optimal accuracy-speed tradeoff

“…1 Illustration of an IS for Post-Disturbance TSA Moreover, different system models may require substantially different lengths of observation windows to obtain reliable assessment results. In [17,21,22], a self-adaptive TSA scheme is proposed and adopted to obtain a reliable assessment result as fast as possible. In doing so, emergency control actions can be activated at an early time to timely avoid further instability propagation.…”

“…In credibility check, the credible criterion is generally defined by a number of credible decision parameters, and a limit of the exiting methods [17,21,22] is that those parameters are usually manually set by experience.…”

“…5), which represents a benchmark power system for stability analysis [21]. The synchronous generator at bus 37 is replaced by a wind farm of the same capacity to simulate the impact of renewable energy sources.…”

Towards a novel quantification approach based on smart grid network vulnerability score

“…Thus, we cannot use the CVSS to consider the nature of a network, and the CVSS merely classifies an attack as a local-access attack, an adjacent-network attack, or a general-network attack. Various methods have been proposed to resolve such CVSS-related problems, but these methods have not solved the essential problems, and they do not consider the nature of smart grid network [5][6][7][8][9][10][11][12][13][14].…”

“…Especially, it is difficult to apply existing techniques in order to evaluate the security threats to smart grid. Although several studies [5][6][7][8][9][10][11][12][13][14] have been conducted from various standpoints to solve such problems, they do not present a fundamental solution. Also, there are some researches for SCADA or cyber infrastructure's security assessment, but these results cannot apply to smart grid network directly [15][16][17][18][19][20].…”