PMU Measurement-Based Intelligent Strategy for Power System Controlled Islanding
Controlled islanding is an effective remedy to prevent large-area blackouts in a power system under a critically unstable condition. When and where to separate the power system are the essential issues facing controlled islanding. In this paper, both tasks are studied to ensure higher time efficienc...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-01-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/11/1/143 |
| _version_ | 1798041988947247104 |
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| author | Yi Tang Feng Li Chenyi Zheng Qi Wang Yingjun Wu |
| author_facet | Yi Tang Feng Li Chenyi Zheng Qi Wang Yingjun Wu |
| author_sort | Yi Tang |
| collection | DOAJ |
| description | Controlled islanding is an effective remedy to prevent large-area blackouts in a power system under a critically unstable condition. When and where to separate the power system are the essential issues facing controlled islanding. In this paper, both tasks are studied to ensure higher time efficiency and a better post-splitting restoration effect. A transient stability assessment model based on extreme learning machine (ELM) and trajectory fitting (TF) is constructed to determine the start-up criterion for controlled islanding. This model works through prompt stability status judgment with ELM and selective result amendment with TF to ensure that the assessment is both efficient and accurate. Moreover, a splitting surface searching algorithm, subject to minimal power disruption, is proposed for determination of the controlled islanding implementing locations. A highlight of this algorithm is a proposed modified electrical distance concept defined by active power magnitude and reactance on transmission lines that realize a computational burden reduction without feasible solution loss. Finally, the simulation results and comparison analysis based on the New England 39-bus test system validates the implementation effects of the proposed controlled islanding strategy. |
| first_indexed | 2024-04-11T22:29:14Z |
| format | Article |
| id | doaj.art-61fd0061683948c3813bed25dbbe2377 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T22:29:14Z |
| publishDate | 2018-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-61fd0061683948c3813bed25dbbe23772022-12-22T03:59:33ZengMDPI AGEnergies1996-10732018-01-0111114310.3390/en11010143en11010143PMU Measurement-Based Intelligent Strategy for Power System Controlled IslandingYi Tang0Feng Li1Chenyi Zheng2Qi Wang3Yingjun Wu4School of Electrical Engineering, Southeast University, Nanjing 210096, ChinaSchool of Electrical Engineering, Southeast University, Nanjing 210096, ChinaSchool of Electrical Engineering, Southeast University, Nanjing 210096, ChinaSchool of Electrical Engineering, Southeast University, Nanjing 210096, ChinaCollege of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaControlled islanding is an effective remedy to prevent large-area blackouts in a power system under a critically unstable condition. When and where to separate the power system are the essential issues facing controlled islanding. In this paper, both tasks are studied to ensure higher time efficiency and a better post-splitting restoration effect. A transient stability assessment model based on extreme learning machine (ELM) and trajectory fitting (TF) is constructed to determine the start-up criterion for controlled islanding. This model works through prompt stability status judgment with ELM and selective result amendment with TF to ensure that the assessment is both efficient and accurate. Moreover, a splitting surface searching algorithm, subject to minimal power disruption, is proposed for determination of the controlled islanding implementing locations. A highlight of this algorithm is a proposed modified electrical distance concept defined by active power magnitude and reactance on transmission lines that realize a computational burden reduction without feasible solution loss. Finally, the simulation results and comparison analysis based on the New England 39-bus test system validates the implementation effects of the proposed controlled islanding strategy.http://www.mdpi.com/1996-1073/11/1/143controlled islandingtransient stabilitymachine learningsplitting surface searching algorithm |
| spellingShingle | Yi Tang Feng Li Chenyi Zheng Qi Wang Yingjun Wu PMU Measurement-Based Intelligent Strategy for Power System Controlled Islanding Energies controlled islanding transient stability machine learning splitting surface searching algorithm |
| title | PMU Measurement-Based Intelligent Strategy for Power System Controlled Islanding |
| title_full | PMU Measurement-Based Intelligent Strategy for Power System Controlled Islanding |
| title_fullStr | PMU Measurement-Based Intelligent Strategy for Power System Controlled Islanding |
| title_full_unstemmed | PMU Measurement-Based Intelligent Strategy for Power System Controlled Islanding |
| title_short | PMU Measurement-Based Intelligent Strategy for Power System Controlled Islanding |
| title_sort | pmu measurement based intelligent strategy for power system controlled islanding |
| topic | controlled islanding transient stability machine learning splitting surface searching algorithm |
| url | http://www.mdpi.com/1996-1073/11/1/143 |
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