Data-Driven Situation Assessment of Power System Static Voltage Stability

doi:10.3969/j.issn.1000-7229.2020.01.015

Abstract

Abstract: With the increasing complexity of power systems, traditional static voltage stability analysis methods face challenges to meet the accuracy and speed of real-time static stability assessment. In this paper, a data-driven static voltage stability evaluation method is proposed. First, the system state data in different time sections are captured along the time trajectory by simulation tools, and the characteristic attributes with high correlation with voltage stability are screened out. The static load active power margin is used to classify the data. Second, a random forest classifier model integrating multiple decision trees is used to transform the original state label data into stable information and behavior models, and the static voltage stability behavior is sensed and learned from the data. Finally, by querying the classification results of the random forest, the judgment of the static voltage stability is achieved. Simulation results show that the model is feasible and suitable for static voltage stability situation assessment.

Key words: power systems, static voltage stability, data driven, situational awareness, machine learning, random forest

CLC Number:

TM 73

LIANG Xiurui, LIU Daowei, YANG Hongying, LI Weixing, SHAO Guanghui, XU Xingwei, WANG Kefei, LI Zonghan, ZHAO Gaoshang. Data-Driven Situation Assessment of Power System Static Voltage Stability[J]. Electric Power Construction, 2020, 41(1): 126-132.

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