Probabilistic power flow calculation using principal component analysis-based compressive sensing
The increasing scale of the injection of renewable energy has brought about great uncertainty to the operation of power grid. In this situation, probabilistic power flow (PPF) calculation has been introduced to mitigate the low accuracy of traditional deterministic power flow calculation in describi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2022.1056077/full |
| _version_ | 1797952168887582720 |
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| author | Tonghe Wang Tonghe Wang Hong Liang Junwei Cao Yuming Zhao |
| author_facet | Tonghe Wang Tonghe Wang Hong Liang Junwei Cao Yuming Zhao |
| author_sort | Tonghe Wang |
| collection | DOAJ |
| description | The increasing scale of the injection of renewable energy has brought about great uncertainty to the operation of power grid. In this situation, probabilistic power flow (PPF) calculation has been introduced to mitigate the low accuracy of traditional deterministic power flow calculation in describing the operation status and power flow distribution of power systems. Polynomial chaotic expansion (PCE) method has become popular in PPF analysis due to its high efficiency and accuracy, and sparse PCE has increased its capability of tackling the issue of dimension disaster. In this paper, we propose a principal component analysis-based compressive sensing (PCA-CS) algorithm solve the PPF problem. The l1-optimization of CS is used to tackle the dimension disaster of sparse PCE, and PCA is included to further increase the sparsity of expansion coefficient matrix. Theoretical and numerical simulation results show that the proposed method can effectively improve the efficiency of PPF calculation in the case of random inputs with higher dimensions. |
| first_indexed | 2024-04-10T22:42:04Z |
| format | Article |
| id | doaj.art-bc3d7f84fe9e4f16998c1a23e7e3d411 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-10T22:42:04Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-bc3d7f84fe9e4f16998c1a23e7e3d4112023-01-16T04:16:09ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-01-011010.3389/fenrg.2022.10560771056077Probabilistic power flow calculation using principal component analysis-based compressive sensingTonghe Wang0Tonghe Wang1Hong Liang2Junwei Cao3Yuming Zhao4Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou, ChinaJiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou University, Changzhou, ChinaMeihua Holdings Group Co., Ltd., Langfang, ChinaBeijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, ChinaSchool of Computer Science and Software, Zhaoqing University, Zhaoqing, ChinaThe increasing scale of the injection of renewable energy has brought about great uncertainty to the operation of power grid. In this situation, probabilistic power flow (PPF) calculation has been introduced to mitigate the low accuracy of traditional deterministic power flow calculation in describing the operation status and power flow distribution of power systems. Polynomial chaotic expansion (PCE) method has become popular in PPF analysis due to its high efficiency and accuracy, and sparse PCE has increased its capability of tackling the issue of dimension disaster. In this paper, we propose a principal component analysis-based compressive sensing (PCA-CS) algorithm solve the PPF problem. The l1-optimization of CS is used to tackle the dimension disaster of sparse PCE, and PCA is included to further increase the sparsity of expansion coefficient matrix. Theoretical and numerical simulation results show that the proposed method can effectively improve the efficiency of PPF calculation in the case of random inputs with higher dimensions.https://www.frontiersin.org/articles/10.3389/fenrg.2022.1056077/fullprobabilistic power flowprincipal component analysiscompressive sensingrenewable energypolynomial chaos expansion |
| spellingShingle | Tonghe Wang Tonghe Wang Hong Liang Junwei Cao Yuming Zhao Probabilistic power flow calculation using principal component analysis-based compressive sensing Frontiers in Energy Research probabilistic power flow principal component analysis compressive sensing renewable energy polynomial chaos expansion |
| title | Probabilistic power flow calculation using principal component analysis-based compressive sensing |
| title_full | Probabilistic power flow calculation using principal component analysis-based compressive sensing |
| title_fullStr | Probabilistic power flow calculation using principal component analysis-based compressive sensing |
| title_full_unstemmed | Probabilistic power flow calculation using principal component analysis-based compressive sensing |
| title_short | Probabilistic power flow calculation using principal component analysis-based compressive sensing |
| title_sort | probabilistic power flow calculation using principal component analysis based compressive sensing |
| topic | probabilistic power flow principal component analysis compressive sensing renewable energy polynomial chaos expansion |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2022.1056077/full |
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