Regression Model Forecasting for Time-Skew Problems in Power System State Estimation
Regression Model Forecasting for Time-Skew Problems in Power System State Estimation
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DOI：
10.1109/naps58826.2023.10318604
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发表时间：
2023-10
期刊：
影响因子：
--
通讯作者：
Gavin Trevorrow;Ning Zhou
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文献类型：
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作者：
Gavin Trevorrow;Ning Zhou
测量时间偏差对电网静态估计的负面影响因系统运行条件的日益变化而加剧。为了缓解时间偏差问题，提出了一种回归模型预测(RMF)方法来预测时间偏差测量，并提出了一种确定与预测测量相关的权重的置信度区间估计(CIE)方法。通过对IEEE16机68节点模型的蒙特卡罗仿真，将所提出的RMF-CIE方法与几种基准方法进行了比较。观察到，所提出的RMF-CIE一致地实现了平均更准确的状态估计。此外，还发现它的估计精度随着偏斜时间和变化程度的减小而增加。
The negative impact of measurement time skew on the static state estimation of the power grid has been exacerbated by increasing variation of system operating conditions. To mitigate the time skew problem, this paper proposes a regression model forecasting (RMF) method to forecast the time-skewed measurements, along with a confidence interval estimation (CIE) method to determine the weights associated with the forecasted measurements. The proposed RMF-CIE method is compared against several benchmark methods through Monte-Carlo simulation on the IEEE 16-machine, 68-bus model. It was observed that the proposed RMF-CIE consistently achieved more accurate state estimation on average. In addition, it was found that its estimation accuracy increases with the decrease of the skew time and variation levels.