Dynamic Identification of Critical Nodes and Regions in Power Grid Based on Spatio-Temporal Attribute Fusion of Voltage Trajectory
Accurate identification of critical nodes and regions in a power grid is a precondition and guarantee for safety assessment and situational awareness. Existing methods have achieved effective static identification based on the inherent topological and electrical characteristics of the grid. However,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/5/780 |
| _version_ | 1798038465541046272 |
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| author | Xiwei Bai Daowei Liu Jie Tan Hongying Yang Hengfeng Zheng |
| author_facet | Xiwei Bai Daowei Liu Jie Tan Hongying Yang Hengfeng Zheng |
| author_sort | Xiwei Bai |
| collection | DOAJ |
| description | Accurate identification of critical nodes and regions in a power grid is a precondition and guarantee for safety assessment and situational awareness. Existing methods have achieved effective static identification based on the inherent topological and electrical characteristics of the grid. However, they ignore the variations of these critical nodes and regions over time and are not appropriate for online monitoring. To solve this problem, a novel data-driven dynamic identification scheme is proposed in this paper. Three temporal and three spatial attributes are extracted from their corresponding voltage phasor sequences and integrated via Gini-coefficient and Spearman correlation coefficient to form node importance and relevance assessment indices. Critical nodes and regions can be identified dynamically through importance ranking and clustering on the basis of these two indices. The validity and applicability of the proposed method pass the test on various situations of the IEEE-39 benchmark system, showing that this method can identify the critical nodes and regions, locate the potential disturbance source accurately, and depict the variation of node/region criticality dynamically. |
| first_indexed | 2024-04-11T21:40:35Z |
| format | Article |
| id | doaj.art-afec284005fd4462aadf061fab0be5ac |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T21:40:35Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-afec284005fd4462aadf061fab0be5ac2022-12-22T04:01:36ZengMDPI AGEnergies1996-10732019-02-0112578010.3390/en12050780en12050780Dynamic Identification of Critical Nodes and Regions in Power Grid Based on Spatio-Temporal Attribute Fusion of Voltage TrajectoryXiwei Bai0Daowei Liu1Jie Tan2Hongying Yang3Hengfeng Zheng4Institute of Automation, Chinese Academy of Sciences, Beijing 100190, ChinaChina Electric Power Research Institute, Beijing 100192, ChinaInstitute of Automation, Chinese Academy of Sciences, Beijing 100190, ChinaChina Electric Power Research Institute, Beijing 100192, ChinaChina Electric Power Research Institute, Beijing 100192, ChinaAccurate identification of critical nodes and regions in a power grid is a precondition and guarantee for safety assessment and situational awareness. Existing methods have achieved effective static identification based on the inherent topological and electrical characteristics of the grid. However, they ignore the variations of these critical nodes and regions over time and are not appropriate for online monitoring. To solve this problem, a novel data-driven dynamic identification scheme is proposed in this paper. Three temporal and three spatial attributes are extracted from their corresponding voltage phasor sequences and integrated via Gini-coefficient and Spearman correlation coefficient to form node importance and relevance assessment indices. Critical nodes and regions can be identified dynamically through importance ranking and clustering on the basis of these two indices. The validity and applicability of the proposed method pass the test on various situations of the IEEE-39 benchmark system, showing that this method can identify the critical nodes and regions, locate the potential disturbance source accurately, and depict the variation of node/region criticality dynamically.https://www.mdpi.com/1996-1073/12/5/780critical nodecritical regionspatio-temporal attribute fusionnode voltage trajectory |
| spellingShingle | Xiwei Bai Daowei Liu Jie Tan Hongying Yang Hengfeng Zheng Dynamic Identification of Critical Nodes and Regions in Power Grid Based on Spatio-Temporal Attribute Fusion of Voltage Trajectory Energies critical node critical region spatio-temporal attribute fusion node voltage trajectory |
| title | Dynamic Identification of Critical Nodes and Regions in Power Grid Based on Spatio-Temporal Attribute Fusion of Voltage Trajectory |
| title_full | Dynamic Identification of Critical Nodes and Regions in Power Grid Based on Spatio-Temporal Attribute Fusion of Voltage Trajectory |
| title_fullStr | Dynamic Identification of Critical Nodes and Regions in Power Grid Based on Spatio-Temporal Attribute Fusion of Voltage Trajectory |
| title_full_unstemmed | Dynamic Identification of Critical Nodes and Regions in Power Grid Based on Spatio-Temporal Attribute Fusion of Voltage Trajectory |
| title_short | Dynamic Identification of Critical Nodes and Regions in Power Grid Based on Spatio-Temporal Attribute Fusion of Voltage Trajectory |
| title_sort | dynamic identification of critical nodes and regions in power grid based on spatio temporal attribute fusion of voltage trajectory |
| topic | critical node critical region spatio-temporal attribute fusion node voltage trajectory |
| url | https://www.mdpi.com/1996-1073/12/5/780 |
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