arXiv 2001.075972020-01-21eess.SYcs.SY

电网元件故障的贝叶斯分层建模方法

Bayesian Hierarchical Methods for Modeling Electrical Grid Component Failures

Laurel N. Dunn, Ioanna Kavvada, Mathilde Badoual, Scott J. Moura

摘要：

电网组件的故障概率通常使用参数模型来估计，该模型可以利用运行电网数据。这项工作制定了贝叶斯分层框架，旨在整合数据和领域的专业知识，以了解区域电力系统的故障属性，在个别组件的预期性能的变化引起的故障过程是异构的和不确定的。我们使用贝叶斯方法来拟合故障模型，以模拟中产生的故障数据。我们通过评估数据生成模型，贝叶斯分层模型和最大似然参数估计之间的差异来测试我们的算法。我们评估如何以及每个模型可以近似个别组件的故障特性，以及整个系统。最后，我们定义了一个升级策略，以实现有针对性的降低风险敞口，并比较每个模型推荐的升级幅度。

英文摘要：

Failure probabilities for grid components are often estimated using parametric models which can capitalize on operational grid data. This work formulates a Bayesian hierarchical framework designed to integrate data and domain expertise to understand the failure properties of a regional power system, where variability in the expected performance of individual components gives rise to failure processes that are heterogeneous and uncertain. We use Bayesian methods to fit failure models to failure data generated in simulation. We test our algorithm by evaluating differences between the data-generating model, our Bayesian hierarchical model, and maximum likelihood parameter estimates. We evaluate how well each model can approximate the failure properties of individual components, and of the system overall. Finally, we define an upgrade policy for achieving targeted reductions in risk exposure, and compare the magnitude of upgrades recommended by each model.

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