Decision Tree for Online Voltage Stability Margin Assessment Using C4.5 and Relief-F Algorithms

Xiangfei Meng, Pei Zhang and Dahai Zhang
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Xiangfei Meng: School of Electrical Engineering, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing 100044, China
Pei Zhang: School of Electrical Engineering, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing 100044, China
Dahai Zhang: School of Electrical Engineering, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing 100044, China

Energies, 2020, vol. 13, issue 15, 1-13

Abstract: 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: voltage stability margin (VSM) assessment; machine learning; decision tree (DT) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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