2014
DOI: 10.1007/s00500-014-1544-x
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Assessment of voltage stability margin by comparing various support vector regression models

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Cited by 21 publications
(12 citation statements)
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“…A well‐trained VSM estimation model can be applied directly to practical applications. Existing data‐driven approaches include multilinear regression model (MLRM), 18,19 regression tree (RT), 20 artificial neural network (ANN), 7,9,21,22 support vector regression (SVR), 10,23 and curve fitting with relationship exploration 4 . Two key factors of accurate VSM estimation are feature selection and model construction.…”
Section: Introductionmentioning
confidence: 99%
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“…A well‐trained VSM estimation model can be applied directly to practical applications. Existing data‐driven approaches include multilinear regression model (MLRM), 18,19 regression tree (RT), 20 artificial neural network (ANN), 7,9,21,22 support vector regression (SVR), 10,23 and curve fitting with relationship exploration 4 . Two key factors of accurate VSM estimation are feature selection and model construction.…”
Section: Introductionmentioning
confidence: 99%
“…Through experiments, the feature set which is comprised of voltage magnitude and phase angle manifests itself as the best feature combination. In literature, 23 the active and reactive power injections of each generator and load bus are selected and two types of SVR with different kernels are analyzed. With appropriate parameter tuning, SVR shows better performance than ANN and extreme learning machine (ELM).…”
Section: Introductionmentioning
confidence: 99%
“…The proposed technique has been tested on New England-39 bus test system and the obtained results indicate the efficiency of the proposed method. 27 Keywords Load shedding • Multiple-deme parallel genetic algorithm • Neural network • Voltage stability 30 1 IntroductionRecent blackouts related to voltage collapse around the world have significantly increased the importance of fast and accurate voltage stability assessment and control (Naganathan and Babulal, 2019;Suganyadevi et al, 2016). Generally, there are two ways to deal with voltage instability in power systems which are classified as preventive and corrective actions.Preventive actions are taken in a pre-contingency condition in order to increase the voltage stability margin while corrective actions are usually taken in a given post-contingency condition in order to restore system stability (Ahmadi and Alinejad-Beromi, 2015).…”
mentioning
confidence: 99%
“…Recent blackouts related to voltage collapse around the world have significantly increased the importance of fast and accurate voltage stability assessment and control (Naganathan and Babulal, 2019;Suganyadevi et al, 2016). Generally, there are two ways to deal with voltage instability in power systems which are classified as preventive and corrective actions.…”
mentioning
confidence: 99%
“…The adaptive neuro-fuzzy interface models (ANFIS) are reported in [17][18] respectively. Various regression modes are investigated in [19].…”
Section: Introductionmentioning
confidence: 99%