LAPSE:2023.29393
Published Article
LAPSE:2023.29393
A Data-Driven and Data-Based Framework for Online Voltage Stability Assessment Using Partial Mutual Information and Iterated Random Forest
April 13, 2023
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.
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.
Record ID
Keywords
iterated random forest, online assessment, partial mutual information, voltage stability margin
Suggested Citation
Liu S, Shi R, Huang Y, Li X, Li Z, Wang L, Mao D, Liu L, Liao S, Zhang M, Yan G, Liu L. A Data-Driven and Data-Based Framework for Online Voltage Stability Assessment Using Partial Mutual Information and Iterated Random Forest. (2023). LAPSE:2023.29393
Author Affiliations
Liu S: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
Shi R: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China [ORCID]
Huang Y: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
Li X: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
Li Z: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China [ORCID]
Wang L: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
Mao D: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
Liu L: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
Liao S: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Zhang M: School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Yan G: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
Liu L: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
Shi R: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China [ORCID]
Huang Y: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
Li X: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
Li Z: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China [ORCID]
Wang L: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
Mao D: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
Liu L: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
Liao S: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Zhang M: School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Yan G: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
Liu L: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
Journal Name
Energies
Volume
14
Issue
3
First Page
715
Year
2021
Publication Date
2021-01-30
ISSN
1996-1073
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PII: en14030715, Publication Type: Journal Article
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LAPSE:2023.29393
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