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Bad data identification for power systems state estimation based on data-driven and interval analysis
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DOI：10.1016/j.epsr.2022.109088.png)
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摘要
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A bad data identification method based on data-driven and interval analysis is proposed to address the identi-fication problem when state estimator malfunction happens. The proposed method combines the industry allowable error standard with affine arithmetic-based interval power flow to generate interval samples (INTSs) and then searches for an INTS with the maximum normal measurements to identify bad data. Compared with the identification method based on state estimation, the method avoids the impact of residual smearing effect on threshold setting and identification accuracy. Compared with the data-driven identification methods based on Monte Carlo samples, this method transforms a large number of nonlinear power flow calculations into a small number of interval linear programming problems and resolves the tradeoff of sample granularity between identification accuracy and identification speed. The numerical tests on IEEE-14, 30, 69, 118, and 300 cases demonstrated that this method has higher accuracy and speed. The bad data identification of a 110 kV distri-bution network snapshot in a Chinese city proves the feasibility of the method on the actual data.
Keyword:
Bad data identification
State estimation
Interval power flow
Monte Carlo
Data -driven
期刊
IF:
4.2
论文数:
1.2W
被引数:
2.2W
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引用论文
Normalized Deleted Residual Test for Identifying Interacting Bad Data in Power System State Estimation电力系统状态估计中识别交互不良数据的归一化删除残差检验
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