Unsupervised Machine Learning Approach to Enhance Online Voltage Security Assessment Based on Synchrophasor Data
Han GaoSchool of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin, ChinaDeyou YangSchool of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin, ChinaYanling LvSchool of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin, ChinaLixin WangSchool of Electrical Engineering, Northeast Electric Power University, Jilin, China
2025en
ABI
Аннотация
The accuracy and reliability of the Q/V sensitivity for voltage security assessment is influenced by the outliers present in the calculation results. An unsupervised machine learning approach, empirical- cumulative- distribution- based outlier detection (ECOD), is introduced in this letter to detect and eliminate outliers to address this issue. A comparison of the results with those of the proposed approaches on the standard test power system CSEE-VS demonstrate that, compared with advanced outlier detection algorithms, ECOD can eliminate outliers from the Q/V sensitivities with higher accuracy and less computation time and realize online voltage security assessment with superior accuracy and reliability.
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