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Parameter errors and gross errors detection, identification and correction

机译:参数错误和严重错误的检测,识别和纠正

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Gross Errors and parameter errors can cause a significant degradation of the results provided by a state estimator, meanly when both appear simultaneously. The majority of methods developed to treat these errors do not consider that both appear simultaneously. This paper proposes an approach that allows detection, identification and correction of parameter errors and gross errors, even when both appear simultaneously. The proposed approach works in two Phases: Phase 1 — detection, identification and correction of network branch parameter (series and shunt admittances) errors using several measurement snapshots; and Phase 2 — gross errors detection, identification and correction. If any gross error was identified in Phase 2, Phase 1 is processed again in order to improve the quality of the parameter estimates. The proposed approach is based on previous authors' researches whose main points will be presented in this paper. Simulation results have sown the effectiveness of the proposed approach even when the gross errors are incident to the branch with parameter errors.
机译:粗略误差和参数错误可能导致状态估计器提供的结果的显着降低,均本意味着当两者同时出现时。为治疗这些错误而开发的大多数方法都不认为两者同时出现。本文提出了一种允许检测,识别和校正参数误差和粗略误差的方法,即使两者同时出现。所提出的方法分为两个阶段:第1阶段 - 使用多个测量快照的网络分支参数(系列和分流入读数)误差的检测,识别和校正;和第2阶段 - 粗略误差检测,识别和校正。如果在阶段2中识别出任何总误差,则再次处理阶段1以提高参数估计的质量。拟议的方法是基于先前的作者的研究,其主要观点将在本文中提出。即使在具有参数错误的分支到分支的情况下,仿真结果也播种了所提出的方法的有效性。

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