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State Estimation in Power Distribution Systems Based on Ensemble Kalman Filtering

机译:基于集合卡尔曼滤波的配电系统状态估计

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摘要

In this paper, a past-aware state estimation (PASE) method (for static state estimation) is proposed for power distribution systems, which takes previous estimates into account to improve the accuracy of the current one, using an Ensemble Kalman Filter (EnKF). Fewer phasor measurements units are needed to achieve the same estimation error target than snapshot-based methods. Furthermore, contrary to existing methods, the proposed approach does not embed power flow equations into the state estimator, thus making it a versatile technique. The theoretical formulation of the EnKF-based PASE presented in the paper has been validated considering a 33-bus distribution system and using power consumption traces from real households.
机译:本文提出了一种用于配电系统的过去状态估计(PASE)方法(用于静态估计),该方法使用集成卡尔曼滤波器(EnKF)考虑了先前的估计以提高当前估计的准确性。 。与基于快照的方法相比,需要更少的相量测量单元即可达到相同的估计误差目标。此外,与现有方法相反,所提出的方法没有将潮流方程嵌入状态估计器中,因此使其成为一种通用技术。考虑到33总线配电系统并使用真实家庭的电力消耗轨迹,已经验证了本文中基于EnKF的PASE的理论公式。

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