基于贝叶斯方法的在不确定发电机参数下的强迫振荡源定位
A Bayesian Approach to Forced Oscillation Source Location Given Uncertain Generator Parameters
AI总结：
本文提出了一种基于贝叶斯方法的强迫振荡源定位方法，通过两阶段最大后验优化来确定在不确定模型前提下最可能的振荡源。该方法利用频域等效电路表示系统，并采用数值方法使其适用于实时应用，在存在PMU测量噪声、高发电机参数不确定性和同时发生多个强迫振荡时表现良好。
摘要：
由于强迫振荡是动态电力系统模型的外生因素，模型本身无法预测何时或在哪里会发生强迫振荡。因此，定位这些振荡的来源是一个具有挑战性的问题，需要能够利用实时电力系统数据来追踪观察到的振荡并追溯到其源头的分析方法。这个问题的难度加剧了由于给定电力系统模型的参数可能从略微不确定到完全未知。在本文中，采用一种贝叶斯框架，通过两阶段最大后验优化程序，用于在不确定的先验模型下确定强迫振荡的最可能来源。该方法利用频域中的等效电路表示系统，并采用数值方法使问题适合实时应用。所推导的框架在存在PMU测量噪声、高发电机参数不确定性和同时发生多个强迫振荡时表现出色。该方法在具有单个强迫振荡源的4节点系统和具有多个振荡源的WECC 179节点系统上进行了测试。
英文摘要：
Since forced oscillations are exogenous to dynamic power system models, the models by themselves cannot predict when or where a forced oscillation will occur. Locating the sources of these oscillations, therefore, is a challenging problem which requires analytical methods capable of using real time power system data to trace an observed oscillation back to its source. The difficulty of this problem is exacerbated by the fact that the parameters associated with a given power system model can range from slightly uncertain to entirely unknown. In this paper, a Bayesian framework, via a two-stage Maximum A Posteriori optimization routine, is employed in order to locate the most probable source of a forced oscillation given an uncertain prior model. The approach leverages an equivalent circuit representation of the system in the frequency domain and employs a numerical procedure which makes the problem suitable for real time application. The derived framework lends itself to successful performance in the presence of PMU measurement noise, high generator parameter uncertainty, and multiple forced oscillations occurring simultaneously. The approach is tested on a 4-bus system with a single forced oscillation source and on the WECC 179-bus system with multiple oscillation sources.
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