Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble
Increased integration of renewable energy sources brings new challenges to the secure and stable power system operation. Operational challenges emanating from the reduced system inertia, in particular, will have important repercussions on the power system transient stability assessment (TSA). At the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/11/3148 |
| _version_ | 1797532318023286784 |
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| author | Petar Sarajcev Antonijo Kunac Goran Petrovic Marin Despalatovic |
| author_facet | Petar Sarajcev Antonijo Kunac Goran Petrovic Marin Despalatovic |
| author_sort | Petar Sarajcev |
| collection | DOAJ |
| description | Increased integration of renewable energy sources brings new challenges to the secure and stable power system operation. Operational challenges emanating from the reduced system inertia, in particular, will have important repercussions on the power system transient stability assessment (TSA). At the same time, a rise of the “big data” in the power system, from the development of wide area monitoring systems, introduces new paradigms for dealing with these challenges. Transient stability concerns are drawing attention of various stakeholders as they can be the leading causes of major outages. The aim of this paper is to address the power system TSA problem from the perspective of data mining and machine learning (ML). A novel 3.8 GB open dataset of time-domain phasor measurements signals is built from dynamic simulations of the IEEE New England 39-bus test case power system. A data processing pipeline is developed for features engineering and statistical post-processing. A complete ML model is proposed for the TSA analysis, built from a denoising stacked autoencoder and a voting ensemble classifier. Ensemble consist of pooling predictions from a support vector machine and a random forest. Results from the classifier application on the test case power system are reported and discussed. The ML application to the TSA problem is promising, since it is able to ingest huge amounts of data while retaining the ability to generalize and support real-time decisions. |
| first_indexed | 2024-03-10T10:57:26Z |
| format | Article |
| id | doaj.art-4fe06aca1aae4738b11834edec5848a6 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T10:57:26Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-4fe06aca1aae4738b11834edec5848a62023-11-21T21:44:22ZengMDPI AGEnergies1996-10732021-05-011411314810.3390/en14113148Power System Transient Stability Assessment Using Stacked Autoencoder and Voting EnsemblePetar Sarajcev0Antonijo Kunac1Goran Petrovic2Marin Despalatovic3Department of Power Engineering, University of Split, FESB, 21000 Split, CroatiaDepartment of Power Engineering, University of Split, FESB, 21000 Split, CroatiaDepartment of Power Engineering, University of Split, FESB, 21000 Split, CroatiaDepartment of Power Engineering, University of Split, FESB, 21000 Split, CroatiaIncreased integration of renewable energy sources brings new challenges to the secure and stable power system operation. Operational challenges emanating from the reduced system inertia, in particular, will have important repercussions on the power system transient stability assessment (TSA). At the same time, a rise of the “big data” in the power system, from the development of wide area monitoring systems, introduces new paradigms for dealing with these challenges. Transient stability concerns are drawing attention of various stakeholders as they can be the leading causes of major outages. The aim of this paper is to address the power system TSA problem from the perspective of data mining and machine learning (ML). A novel 3.8 GB open dataset of time-domain phasor measurements signals is built from dynamic simulations of the IEEE New England 39-bus test case power system. A data processing pipeline is developed for features engineering and statistical post-processing. A complete ML model is proposed for the TSA analysis, built from a denoising stacked autoencoder and a voting ensemble classifier. Ensemble consist of pooling predictions from a support vector machine and a random forest. Results from the classifier application on the test case power system are reported and discussed. The ML application to the TSA problem is promising, since it is able to ingest huge amounts of data while retaining the ability to generalize and support real-time decisions.https://www.mdpi.com/1996-1073/14/11/3148power system stabilitytransient stability assessmenttransient stability indexmachine learningdeep learningautoencoder |
| spellingShingle | Petar Sarajcev Antonijo Kunac Goran Petrovic Marin Despalatovic Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble Energies power system stability transient stability assessment transient stability index machine learning deep learning autoencoder |
| title | Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble |
| title_full | Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble |
| title_fullStr | Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble |
| title_full_unstemmed | Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble |
| title_short | Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble |
| title_sort | power system transient stability assessment using stacked autoencoder and voting ensemble |
| topic | power system stability transient stability assessment transient stability index machine learning deep learning autoencoder |
| url | https://www.mdpi.com/1996-1073/14/11/3148 |
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