电力系统元件识别的贝叶斯框架
A Bayesian Framework for Power System Components Identification
摘要：
拥有电力系统组件（如发电机和负载或辅助设备）的实际模型对于正确评估电力系统运行状态和建立稳定裕度至关重要。然而，电力系统操作员通常具有关于电力系统组件参数的实际值的有限信息。即使有模型可用，其操作参数和控制设置也是随时间变化的，并且需要实时识别。理想情况下，这些参数应该从测量数据中识别，例如相量测量单元（PMU）信号。然而，在不存在瞬态动态的情况下，从环境测量中做到这一点是具有挑战性的，因为这种信号的信噪比（SNR）不一定很大。本文设计了一种基于PMU环境数据的电力系统元件参数在线辨识的贝叶斯框架，该框架在信噪比低至5的情况下具有可靠的性能，对某些参数甚至在单位信噪比下也能给出良好的估计。我们支持一个强大的和时间有效的数值方法的框架。我们说明了同步发电机的例子的方法的效率。
英文摘要：
Having actual models for power system components (such as generators and loads or auxiliary equipment) is vital to correctly assess the power system operating state and to establish stability margins. However, power system operators often have limited information about the actual values for power system component parameters. Even when a model is available, its operating parameters and control settings are time-dependent and subject to real-time identification. Ideally, these parameters should be identified from measurement data, such as phasor measurement unit (PMU) signals. However, it is challenging to do this from the ambient measurements in the absence of transient dynamics since the signal-to-noise ratio (SNR) for such signals is not necessarily large. In this paper, we design a Bayesian framework for on-line identification of power system component parameters based on ambient PMU data, which has reliable performance for SNR as low as five and for certain parameters can give good estimations even for unit SNR. We support the framework with a robust and time-efficient numerical method. We illustrate the approach efficiency on a synchronous generator example.
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