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      Non-parametric statistics-based predictor enabling online transient stability assessment

      1 , 1 , , 1 , 1 , 2 , 2

      IET Generation, Transmission & Distribution

      The Institution of Engineering and Technology

      fault diagnosis, power system transient stability, regression analysis, feature selection, phasor measurement, reduced multifeature space, severity indicators, post-fault stability margin, offline-trained predictor, nonparametric statistics-based predictor, online transient stability assessment, transient instability, critical clearing time-based stability margin index, input features, TSA predictor, nonparametric additive model, weakly correlated indicators, two-stage feature selection, fault contingencies, response-based online TSA, nonparametric independence screening, phasor measurement unit measurements, generator buses, 756-bus transmission system, modified New England 39-bus system

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          Abstract

          Online transient stability assessment (TSA) is of great necessity for fast awareness of transient instability caused by fault contingencies. In this paper, a non-parametric statistics based scheme is proposed for response-based online TSA. A critical clearing time-based stability margin index is defined as the predictive output and 14 kinds of severity indicators are proposed as input features for the TSA predictor. With no prior knowledge of the correlation structure, the non-parametric additive model is used as the basis of the predictor. To screen out the weakly correlated indicators and reduce the dimensionality of the input space, two-stage feature selection is fulfilled by non-parametric independence screening and group Lasso penalised regression successively. The predictor is then learnt by least-squares regression in the reduced multi-feature space. With phasor measurement unit measurements at generator buses, severity indicators can be computed in the real-time and fast evaluation of post-fault stability margin can be made by the offline-trained predictor. The effectiveness of the proposed non-parametric statistics based scheme is demonstrated in a modified New England 39-bus system and a practical 756-bus transmission system in China.

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          Most cited references 30

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          Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models

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            Variable selection in nonparametric additive models

            We consider a nonparametric additive model of a conditional mean function in which the number of variables and additive components may be larger than the sample size but the number of nonzero additive components is "small" relative to the sample size. The statistical problem is to determine which additive components are nonzero. The additive components are approximated by truncated series expansions with B-spline bases. With this approximation, the problem of component selection becomes that of selecting the groups of coefficients in the expansion. We apply the adaptive group Lasso to select nonzero components, using the group Lasso to obtain an initial estimator and reduce the dimension of the problem. We give conditions under which the group Lasso selects a model whose number of components is comparable with the underlying model, and the adaptive group Lasso selects the nonzero components correctly with probability approaching one as the sample size increases and achieves the optimal rate of convergence. The results of Monte Carlo experiments show that the adaptive group Lasso procedure works well with samples of moderate size. A data example is used to illustrate the application of the proposed method.
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              Support Vector Machine-Based Algorithm for Post-Fault Transient Stability Status Prediction Using Synchronized Measurements

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                Author and article information

                Affiliations
                [1 ] College of Electrical Engineering and Information Technology, Sichuan University , Chengdu, People's Republic of China
                [2 ] Department of Electrical and Computer Engineering, Michigan State University , East Lansing, Michigan, USA
                Contributors
                Journal
                IET-GTD
                IET Generation, Transmission & Distribution
                IET Gener. Transm. Distrib.
                The Institution of Engineering and Technology
                1751-8687
                1751-8695
                5 October 2018
                5 November 2018
                27 November 2018
                : 12
                : 21
                : 5761-5769
                IET-GTD.2018.5802 GTD.2018.5802.R1
                10.1049/iet-gtd.2018.5802

                This is an open access article published by the IET under the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/)

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                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 51437003
                Categories
                Research Article

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