Assessment and classification of grid stability with cost-sensitive stacked ensemble classifier
Smart Grid is an intelligent power grid with a bidirectional flow of electricity and information, that applies intelligent techniques to operate the grid autonomously near the stability limit. An intelligent technique is developed to identify and predict the abnormalities due to changes in customer...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-10-01
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| Series: | Automatika |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2023.2218164 |
| _version_ | 1827309018324926464 |
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| author | Karthikeyan Ramasamy Arivoli Sundaramurthy Durgadevi Velusamy |
| author_facet | Karthikeyan Ramasamy Arivoli Sundaramurthy Durgadevi Velusamy |
| author_sort | Karthikeyan Ramasamy |
| collection | DOAJ |
| description | Smart Grid is an intelligent power grid with a bidirectional flow of electricity and information, that applies intelligent techniques to operate the grid autonomously near the stability limit. An intelligent technique is developed to identify and predict the abnormalities due to changes in customer behaviour and the unexpected disruption in the grid. A cost-sensitive stacked ensemble classifier (CS-SEC) is proposed for predicting the operations in smart grid that combines four cost-sensitive base classifiers, namely Extreme gradient boosting, Naive Bayes, Nu-support vector machine and Random forest at level-1 and the support vector machine as the meta classifier in level-2. The meta classifier uses the probability of prediction of the first-level classifiers with stratified 5-fold cross-validation to predict the decentralized smart grid stability. The proposed stacked ensemble classifier achieved an accuracy of 98.6% with specificity, recall and precision of 98.34%, 99.0% and 99.06%, respectively. Extensive experimental evaluation and results show that the proposed CS-SEC provides an accurate prediction of grid stability compared with other state-of-the-art models. The results reveal the robustness and competency of the proposed CS-SECs with optimized parameters. |
| first_indexed | 2024-03-10T15:57:51Z |
| format | Article |
| id | doaj.art-6e2c8ae2ac0f4d50acb17ceb177bfbdc |
| institution | Directory Open Access Journal |
| issn | 0005-1144 1848-3380 |
| language | English |
| last_indexed | 2024-04-24T19:20:46Z |
| publishDate | 2023-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj.art-6e2c8ae2ac0f4d50acb17ceb177bfbdc2024-03-25T18:18:03ZengTaylor & Francis GroupAutomatika0005-11441848-33802023-10-0164478379710.1080/00051144.2023.2218164Assessment and classification of grid stability with cost-sensitive stacked ensemble classifierKarthikeyan Ramasamy0Arivoli Sundaramurthy1Durgadevi Velusamy2Department of Electrical and Electronics Engineering, M. Kumarasamy College of Engineering, Thalavapalayam, IndiaDepartment of Electrical and Electronics Engineering, M. Kumarasamy College of Engineering, Thalavapalayam, IndiaDepartment of Information Technology, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, IndiaSmart Grid is an intelligent power grid with a bidirectional flow of electricity and information, that applies intelligent techniques to operate the grid autonomously near the stability limit. An intelligent technique is developed to identify and predict the abnormalities due to changes in customer behaviour and the unexpected disruption in the grid. A cost-sensitive stacked ensemble classifier (CS-SEC) is proposed for predicting the operations in smart grid that combines four cost-sensitive base classifiers, namely Extreme gradient boosting, Naive Bayes, Nu-support vector machine and Random forest at level-1 and the support vector machine as the meta classifier in level-2. The meta classifier uses the probability of prediction of the first-level classifiers with stratified 5-fold cross-validation to predict the decentralized smart grid stability. The proposed stacked ensemble classifier achieved an accuracy of 98.6% with specificity, recall and precision of 98.34%, 99.0% and 99.06%, respectively. Extensive experimental evaluation and results show that the proposed CS-SEC provides an accurate prediction of grid stability compared with other state-of-the-art models. The results reveal the robustness and competency of the proposed CS-SECs with optimized parameters.https://www.tandfonline.com/doi/10.1080/00051144.2023.2218164Classification algorithmsmachine learningSmart Gridsstability analysissupport vector machines |
| spellingShingle | Karthikeyan Ramasamy Arivoli Sundaramurthy Durgadevi Velusamy Assessment and classification of grid stability with cost-sensitive stacked ensemble classifier Automatika Classification algorithms machine learning Smart Grids stability analysis support vector machines |
| title | Assessment and classification of grid stability with cost-sensitive stacked ensemble classifier |
| title_full | Assessment and classification of grid stability with cost-sensitive stacked ensemble classifier |
| title_fullStr | Assessment and classification of grid stability with cost-sensitive stacked ensemble classifier |
| title_full_unstemmed | Assessment and classification of grid stability with cost-sensitive stacked ensemble classifier |
| title_short | Assessment and classification of grid stability with cost-sensitive stacked ensemble classifier |
| title_sort | assessment and classification of grid stability with cost sensitive stacked ensemble classifier |
| topic | Classification algorithms machine learning Smart Grids stability analysis support vector machines |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2023.2218164 |
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