LAPSE:2023.24439
Published Article
LAPSE:2023.24439
Decision Tree for Online Voltage Stability Margin Assessment Using C4.5 and Relief-F Algorithms
Xiangfei Meng, Pei Zhang, Dahai Zhang
March 28, 2023
In practical power system operation, knowing the voltage stability limits of the system is important. This paper proposes using a decision tree (DT) to extract guidelines through offline study results for assessing system voltage stability status online. Firstly, a sample set of DTs is determined offline by active power injection and bus voltage magnitude (P-V) curve analysis. Secondly, participation factor (PF) analysis and the Relief-F algorithm are used successively for attribute selection, which takes both the physical significance and the classification capabilities into consideration. Finally, the C4.5 algorithm is used to build the DT because it is more suitable for handling continuous variables. A practical power system is implemented to verify the feasibility of the proposed online voltage stability margin (VSM) assessment framework. Study results indicate that the operating guidelines extracted from the DT can help power system operators assess real time VSM effectively.
Keywords
decision tree (DT), Machine Learning, voltage stability margin (VSM) assessment
Suggested Citation
Meng X, Zhang P, Zhang D. Decision Tree for Online Voltage Stability Margin Assessment Using C4.5 and Relief-F Algorithms. (2023). LAPSE:2023.24439
Author Affiliations
Meng X: School of Electrical Engineering, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing 100044, China [ORCID]
Zhang P: School of Electrical Engineering, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing 100044, China [ORCID]
Zhang D: School of Electrical Engineering, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing 100044, China
Journal Name
Energies
Volume
13
Issue
15
Article Number
E3824
Year
2020
Publication Date
2020-07-25
Published Version
ISSN
1996-1073
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PII: en13153824, Publication Type: Journal Article
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LAPSE:2023.24439
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doi:10.3390/en13153824
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