Identification of Vulnerable Lines in Smart Grid Systems Based on Improved Agglomerative Hierarchical Clustering
The identification of vulnerable lines in smart grid systems is of great significance to increase the stability of the smart grid systems and reduce the occurrence of cascading fault blackouts. Inspired by the machine learning method, this study proposes a vulnerable line identification approach bas...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10041134/ |
| _version_ | 1797905457154621440 |
|---|---|
| author | Liulin Yang Chao Li |
| author_facet | Liulin Yang Chao Li |
| author_sort | Liulin Yang |
| collection | DOAJ |
| description | The identification of vulnerable lines in smart grid systems is of great significance to increase the stability of the smart grid systems and reduce the occurrence of cascading fault blackouts. Inspired by the machine learning method, this study proposes a vulnerable line identification approach based on the improved agglomerative hierarchical clustering algorithm. By jointly considering the topological parameters and the electrical properties, we discuss the vulnerability of the transmission lines and establish the influencing factors. Then, we adopt principal component analysis (PCA) to select the influencing factors and reduce their dimensionality. Finally, an improved agglomerative hierarchical clustering algorithm is proposed and employed to divide the lines to identify the vulnerable lines in the smart grid systems. Experiments over the IEEE 39-bus system demonstrate that our proposed method can efficiently and accurately identify different types of potential vulnerable lines in smart grid systems. |
| first_indexed | 2024-04-10T10:05:37Z |
| format | Article |
| id | doaj.art-597ad42e0894482a9d8da0f60fee48b3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T10:05:37Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-597ad42e0894482a9d8da0f60fee48b32023-02-16T00:00:43ZengIEEEIEEE Access2169-35362023-01-0111135541356310.1109/ACCESS.2023.324380610041134Identification of Vulnerable Lines in Smart Grid Systems Based on Improved Agglomerative Hierarchical ClusteringLiulin Yang0https://orcid.org/0000-0002-6629-1635Chao Li1https://orcid.org/0000-0002-3292-0253College of Electrical Engineering, Guangxi University, Nanning, ChinaCollege of Electrical Engineering, Guangxi University, Nanning, ChinaThe identification of vulnerable lines in smart grid systems is of great significance to increase the stability of the smart grid systems and reduce the occurrence of cascading fault blackouts. Inspired by the machine learning method, this study proposes a vulnerable line identification approach based on the improved agglomerative hierarchical clustering algorithm. By jointly considering the topological parameters and the electrical properties, we discuss the vulnerability of the transmission lines and establish the influencing factors. Then, we adopt principal component analysis (PCA) to select the influencing factors and reduce their dimensionality. Finally, an improved agglomerative hierarchical clustering algorithm is proposed and employed to divide the lines to identify the vulnerable lines in the smart grid systems. Experiments over the IEEE 39-bus system demonstrate that our proposed method can efficiently and accurately identify different types of potential vulnerable lines in smart grid systems.https://ieeexplore.ieee.org/document/10041134/Improved agglomerative hierarchical clusteringvulnerable linesinfluencing factorsmachine learningprincipal component analysis (PCA)smart grid systems |
| spellingShingle | Liulin Yang Chao Li Identification of Vulnerable Lines in Smart Grid Systems Based on Improved Agglomerative Hierarchical Clustering IEEE Access Improved agglomerative hierarchical clustering vulnerable lines influencing factors machine learning principal component analysis (PCA) smart grid systems |
| title | Identification of Vulnerable Lines in Smart Grid Systems Based on Improved Agglomerative Hierarchical Clustering |
| title_full | Identification of Vulnerable Lines in Smart Grid Systems Based on Improved Agglomerative Hierarchical Clustering |
| title_fullStr | Identification of Vulnerable Lines in Smart Grid Systems Based on Improved Agglomerative Hierarchical Clustering |
| title_full_unstemmed | Identification of Vulnerable Lines in Smart Grid Systems Based on Improved Agglomerative Hierarchical Clustering |
| title_short | Identification of Vulnerable Lines in Smart Grid Systems Based on Improved Agglomerative Hierarchical Clustering |
| title_sort | identification of vulnerable lines in smart grid systems based on improved agglomerative hierarchical clustering |
| topic | Improved agglomerative hierarchical clustering vulnerable lines influencing factors machine learning principal component analysis (PCA) smart grid systems |
| url | https://ieeexplore.ieee.org/document/10041134/ |
| work_keys_str_mv | AT liulinyang identificationofvulnerablelinesinsmartgridsystemsbasedonimprovedagglomerativehierarchicalclustering AT chaoli identificationofvulnerablelinesinsmartgridsystemsbasedonimprovedagglomerativehierarchicalclustering |