最小阶潮流模型的实现及SEA模型的时域更新

J. Gregory, R. Keltie, F. D. Caulfield
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引用次数: 0

摘要

提出了一种从声音和振动测量中识别中高频系统参数的时域方法。这项工作是一项初步调查的一部分,目的是加强实验统计能量分析(SEA)。至此,可以看到一种状态空间实现方法可以从实测暂态功率和能量数据中识别一阶潮流模型。具体而言,本征系统实现算法(ERA)可以准确地识别简单系统的最小阶模型。此外,还发现所识别的模型可用于改进系统的现有SEA模型。
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Realization of a Minimum-Order Power Flow Model and SEA Model Updating Using Time Domain Measurements
A time domain method for identifying middle to high frequency system parameters from sound and vibration measurements is presented. This work is part of a beginning investigation with an objective of enhancing experimental Statistical Energy Analysis (SEA). Thus far, it is seen that a state space realization method can be used to identify a first order power flow model from measured transient power and energy data. Specifically, the Eigensystem Realization Algorithm (ERA) is observed to accurately identify a minimum order model for a simple system. Additionally, it is found that the identified model can be used to improve an existing SEA model of the system.
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