EM-TSA: An ensemble machine learning-based transient stability assessment approach for operation of power systems
The transient stability of power systems plays the key role in their smooth operation, which is influenced by many working condition factors. To automatically evaluate transient stability status precisely for power systems remains a practical issue. To realize data-driven evaluation for the transien...
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-02-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023358?viewType=HTML |
| _version_ | 1797870659336929280 |
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| author | Jiuju Shen |
| author_facet | Jiuju Shen |
| author_sort | Jiuju Shen |
| collection | DOAJ |
| description | The transient stability of power systems plays the key role in their smooth operation, which is influenced by many working condition factors. To automatically evaluate transient stability status precisely for power systems remains a practical issue. To realize data-driven evaluation for the transient stability of the power systems, this paper proposes an ensemble machine learning-based assessment approach for transient stability status of power systems, which is named as EM-TSA for short. The experiments prove that the proposed model outperforms each secondary learning model and the traditional deep learning model in terms of accuracy and safety indexes. Considering the effect of noise, the experiments are repeated by adding Gaussian noise to the original test set. The results show that the ensemble learning model can maintain 98.4% accuracy under various noisy environments. In addition, the proposed model is combined with the sample transfer learning algorithm when the system topology is changed. An online update method for transient stability models is proposed, and compared with the previous approaches, the proposed algorithm can adapt to the online update of transient stability assessment models. |
| first_indexed | 2024-04-10T00:30:50Z |
| format | Article |
| id | doaj.art-5842066f36344d3788aabaaaae6619b7 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-10T00:30:50Z |
| publishDate | 2023-02-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-5842066f36344d3788aabaaaae6619b72023-03-15T01:28:03ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-02-012058226824010.3934/mbe.2023358EM-TSA: An ensemble machine learning-based transient stability assessment approach for operation of power systemsJiuju Shen0Mechanical and Electrical Engineering College, Henan Industry and Trade Vocational College, Zhengzhou, ChinaThe transient stability of power systems plays the key role in their smooth operation, which is influenced by many working condition factors. To automatically evaluate transient stability status precisely for power systems remains a practical issue. To realize data-driven evaluation for the transient stability of the power systems, this paper proposes an ensemble machine learning-based assessment approach for transient stability status of power systems, which is named as EM-TSA for short. The experiments prove that the proposed model outperforms each secondary learning model and the traditional deep learning model in terms of accuracy and safety indexes. Considering the effect of noise, the experiments are repeated by adding Gaussian noise to the original test set. The results show that the ensemble learning model can maintain 98.4% accuracy under various noisy environments. In addition, the proposed model is combined with the sample transfer learning algorithm when the system topology is changed. An online update method for transient stability models is proposed, and compared with the previous approaches, the proposed algorithm can adapt to the online update of transient stability assessment models.https://www.aimspress.com/article/doi/10.3934/mbe.2023358?viewType=HTMLpower systemstransient stabilityensemble learningsmart assessment |
| spellingShingle | Jiuju Shen EM-TSA: An ensemble machine learning-based transient stability assessment approach for operation of power systems Mathematical Biosciences and Engineering power systems transient stability ensemble learning smart assessment |
| title | EM-TSA: An ensemble machine learning-based transient stability assessment approach for operation of power systems |
| title_full | EM-TSA: An ensemble machine learning-based transient stability assessment approach for operation of power systems |
| title_fullStr | EM-TSA: An ensemble machine learning-based transient stability assessment approach for operation of power systems |
| title_full_unstemmed | EM-TSA: An ensemble machine learning-based transient stability assessment approach for operation of power systems |
| title_short | EM-TSA: An ensemble machine learning-based transient stability assessment approach for operation of power systems |
| title_sort | em tsa an ensemble machine learning based transient stability assessment approach for operation of power systems |
| topic | power systems transient stability ensemble learning smart assessment |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023358?viewType=HTML |
| work_keys_str_mv | AT jiujushen emtsaanensemblemachinelearningbasedtransientstabilityassessmentapproachforoperationofpowersystems |