Designing a Four-Group Permutational Hybrid Feature Selection Scheme for Transient Stability Prediction Based on Multivariate Trajectory Data

Collecting the optimal transient features (OTFs) from multivariate transient time series data is crucial for ensuring precise and prompt transient analysis (TA) in power systems. Realizing the OTFs requires designing a comprehensive hybrid feature selection scheme (FSS). Hence, this work offers a four-group permutational hybrid FSS called FGPHFSS to select OTFs from transient trajectory data. The FGPHFSS includes four hybrid FSS (4HFSS) groups decorated by filter-wrapper mechanisms. The two 4HFSS groups comprise a relevance-based filter and another two 4HFSS groups derived from a conditional relevance-based filter. The 4HFSS groups includes incremental wrapper methods namely incremental wrapper subset selection (IWSS) and IWSS with replacement (IWSSr), which are fed by the support vector machine (SVM) and twin SVM (TWSVM) classifiers. The nonlinear space of transient data causes the SVM and TWSVM to be equipped with elastic and non-elastic kernels. Generally, we have <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathop {{}_{\text {TWSVM}}^{\text {SVM}} \textrm {IWSS}_{\text {RBF/POL}}^{\text {RBF/DTW}}}\limits ^{\text {Relevance}}$ </tex-math></inline-formula>, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathop {{}_{\text {TWSVM}}^{\text {SVM}} \textrm {IWSSr}_{\text {RBF}/\text {POL}}^{\text {RBF/DTW}} }\limits ^{\text {Relevance}}$ </tex-math></inline-formula>, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathop {{}_{\text {TWSVM}}^{\text {SVM}} \textrm {IWSS}_{\text {RBF/POL}}^{\text {RBF/DTW}}}\limits ^{\text {Cond. Relevance}}$ </tex-math></inline-formula>, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathop {{}_{\text {TWSVM}}^{\text {SVM}} \textrm {IWSSr}_{\text {RBF/POL}}^{\text {RBF/DTW}}}\limits ^{\text {Cond. relevance}}$ </tex-math></inline-formula>, which are permuted in a 24-way manner. After selecting OTFs, the OTFs’ performance in transient stability prediction (TSP) is evaluated by a cross-validation technique. The results show that FGPHFSS has a prediction accuracy of 99 % and a processing time of 101.819 milliseconds for TSP.

Authors

Institutions

Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3592712
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Designing a Four-Group Permutational Hybrid Feature Selection Scheme for Transient Stability Prediction Based on Multivariate Trajectory Data

Seyed Alireza Bashiri Mosavi, Omid Khalaf Beigi
IEEE Access
Traffic Prediction and Management Techniques
article

Designing a Four-Group Permutational Hybrid Feature Selection Scheme for Transient Stability Prediction Based on Multivariate Trajectory Data

Seyed Alireza Bashiri Mosavi, Omid Khalaf Beigi
article en

Abstract

Collecting the optimal transient features (OTFs) from multivariate transient time series data is crucial for ensuring precise and prompt transient analysis (TA) in power systems. Realizing the OTFs requires designing a comprehensive hybrid feature selection scheme (FSS). Hence, this work offers a four-group permutational hybrid FSS called FGPHFSS to select OTFs from transient trajectory data. The FGPHFSS includes four hybrid FSS (4HFSS) groups decorated by filter-wrapper mechanisms. The two 4HFSS groups comprise a relevance-based filter and another two 4HFSS groups derived from a conditional relevance-based filter. The 4HFSS groups includes incremental wrapper methods namely incremental wrapper subset selection (IWSS) and IWSS with replacement (IWSSr), which are fed by the support vector machine (SVM) and twin SVM (TWSVM) classifiers. The nonlinear space of transient data causes the SVM and TWSVM to be equipped with elastic and non-elastic kernels. Generally, we have <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathop {{}_{\text {TWSVM}}^{\text {SVM}} \textrm {IWSS}_{\text {RBF/POL}}^{\text {RBF/DTW}}}\limits ^{\text {Relevance}}$ </tex-math></inline-formula>, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathop {{}_{\text {TWSVM}}^{\text {SVM}} \textrm {IWSSr}_{\text {RBF}/\text {POL}}^{\text {RBF/DTW}} }\limits ^{\text {Relevance}}$ </tex-math></inline-formula>, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathop {{}_{\text {TWSVM}}^{\text {SVM}} \textrm {IWSS}_{\text {RBF/POL}}^{\text {RBF/DTW}}}\limits ^{\text {Cond. Relevance}}$ </tex-math></inline-formula>, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathop {{}_{\text {TWSVM}}^{\text {SVM}} \textrm {IWSSr}_{\text {RBF/POL}}^{\text {RBF/DTW}}}\limits ^{\text {Cond. relevance}}$ </tex-math></inline-formula>, which are permuted in a 24-way manner. After selecting OTFs, the OTFs’ performance in transient stability prediction (TSP) is evaluated by a cross-validation technique. The results show that FGPHFSS has a prediction accuracy of 99 % and a processing time of 101.819 milliseconds for TSP.

IEEE AccessVol. 13
Kharazmi University (IR), Buein Zahra Technical University (IR)
Openalex Percentile: Top 80%
Traffic Prediction and Management Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.