A Data-Driven and Data-Based Framework for Online Voltage Stability Assessment Using Partial Mutual Information and Iterated Random Forest

Songkai Liu, Ruoyuan Shi, Yuehua Huang, Xin Li, Zhenhua Li, Lingyun Wang, Dan Mao, Lihuang Liu, Siyang Liao, Menglin Zhang, Guanghui Yan and Lian Liu
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Songkai Liu: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Ruoyuan Shi: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Yuehua Huang: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Xin Li: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Zhenhua Li: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Lingyun Wang: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Dan Mao: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Lihuang Liu: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Siyang Liao: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Menglin Zhang: School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Guanghui Yan: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Lian Liu: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China

Energies, 2021, vol. 14, issue 3, 1-16

Abstract: Due to the rapid development of phasor measurement units (PMUs) and the wide area of interconnection of modern power systems, the security of power systems is confronted with severe challenges. A novel framework based on data for static voltage stability margin (VSM) assessment of power systems is presented. The proposed framework can select the key operation variables as input features for the assessment based on partial mutual information (PMI). Before the feature selection procedure is completed by PMI, a feature preprocessing approach is applied to remove redundant and irrelevant features to improve computational efficiency. Using the selected key variables, a voltage stability assessment (VSA) model based on iterated random forest (IRF) can rapidly provide the relative VSM results. The proposed framework is examined on the IEEE 30-bus system and a practical 1648-bus system, and a desirable assessment performance is demonstrated. In addition, the robustness and computational speed of the proposed framework are also verified. Some impact factors for power system operation are studied in a robustness examination, such as topology change, variation of peak/minimum load, and variation of generator/load power distribution.

Keywords: voltage stability margin; online assessment; partial mutual information; iterated random forest (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: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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