An Online Security Prediction and Control Framework for Modern Power Grids
The proliferation of renewable energy sources distributed generation (RES-DG) into the grid results in time-varying inertia constant. To ensure the security of the grid under varying inertia, techniques for fast security assessment are required. In addition, considering the high penetration of RES-D...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-10-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/20/6639 |
| _version_ | 1797514746452246528 |
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| author | Ifedayo Oladeji Ramon Zamora Tek Tjing Lie |
| author_facet | Ifedayo Oladeji Ramon Zamora Tek Tjing Lie |
| author_sort | Ifedayo Oladeji |
| collection | DOAJ |
| description | The proliferation of renewable energy sources distributed generation (RES-DG) into the grid results in time-varying inertia constant. To ensure the security of the grid under varying inertia, techniques for fast security assessment are required. In addition, considering the high penetration of RES-DG units into the modern grids, security prediction using varying grid features is crucial. The computation burden concerns of conventional time-domain security assessment techniques make it unsuitable for real-time security prediction. This paper, therefore, proposes a fast security monitoring model that includes security prediction and load shedding for security control. The attributes considered in this paper include the load level, inertia constant, fault location, and power dispatched from the renewable energy sources generator. An incremental Naïve Bayes algorithm is applied on the training dataset developed from the responses of the grid to transient stability simulations. An additive Gaussian process regression (GPR) model is proposed to estimate the load shedding required for the predicted insecure states. Finally, an algorithm based on the nodes’ security margin is proposed to determine the optimal node (s) for the load shedding. The average security prediction and load shedding estimation model training times are 1.2 s and 3 s, respectively. The result shows that the proposed model can predict the security of the grid, estimate the amount of load shed required, and determine the specific node for load shedding operation. |
| first_indexed | 2024-03-10T06:36:09Z |
| format | Article |
| id | doaj.art-f4821722d4bb4cd2b89fc92b94e4ab53 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T06:36:09Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-f4821722d4bb4cd2b89fc92b94e4ab532023-11-22T18:06:22ZengMDPI AGEnergies1996-10732021-10-011420663910.3390/en14206639An Online Security Prediction and Control Framework for Modern Power GridsIfedayo Oladeji0Ramon Zamora1Tek Tjing Lie2Electrical and Electronic Engineering Department, Auckland University of Technology (AUT), Auckland 1010, New ZealandElectrical and Electronic Engineering Department, Auckland University of Technology (AUT), Auckland 1010, New ZealandElectrical and Electronic Engineering Department, Auckland University of Technology (AUT), Auckland 1010, New ZealandThe proliferation of renewable energy sources distributed generation (RES-DG) into the grid results in time-varying inertia constant. To ensure the security of the grid under varying inertia, techniques for fast security assessment are required. In addition, considering the high penetration of RES-DG units into the modern grids, security prediction using varying grid features is crucial. The computation burden concerns of conventional time-domain security assessment techniques make it unsuitable for real-time security prediction. This paper, therefore, proposes a fast security monitoring model that includes security prediction and load shedding for security control. The attributes considered in this paper include the load level, inertia constant, fault location, and power dispatched from the renewable energy sources generator. An incremental Naïve Bayes algorithm is applied on the training dataset developed from the responses of the grid to transient stability simulations. An additive Gaussian process regression (GPR) model is proposed to estimate the load shedding required for the predicted insecure states. Finally, an algorithm based on the nodes’ security margin is proposed to determine the optimal node (s) for the load shedding. The average security prediction and load shedding estimation model training times are 1.2 s and 3 s, respectively. The result shows that the proposed model can predict the security of the grid, estimate the amount of load shed required, and determine the specific node for load shedding operation.https://www.mdpi.com/1996-1073/14/20/6639securityincremental machine learningrenewable energy sourcesdistributed generation |
| spellingShingle | Ifedayo Oladeji Ramon Zamora Tek Tjing Lie An Online Security Prediction and Control Framework for Modern Power Grids Energies security incremental machine learning renewable energy sources distributed generation |
| title | An Online Security Prediction and Control Framework for Modern Power Grids |
| title_full | An Online Security Prediction and Control Framework for Modern Power Grids |
| title_fullStr | An Online Security Prediction and Control Framework for Modern Power Grids |
| title_full_unstemmed | An Online Security Prediction and Control Framework for Modern Power Grids |
| title_short | An Online Security Prediction and Control Framework for Modern Power Grids |
| title_sort | online security prediction and control framework for modern power grids |
| topic | security incremental machine learning renewable energy sources distributed generation |
| url | https://www.mdpi.com/1996-1073/14/20/6639 |
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