论文标题
用于建模电网组件故障的贝叶斯分层方法
Bayesian Hierarchical Methods for Modeling Electrical Grid Component Failures
论文作者
论文摘要
网格组件的故障概率通常是使用可以利用操作网格数据的参数模型来估算的。这项工作制定了一个贝叶斯分层框架，旨在集成数据和域专业知识，以了解区域电力系统的故障属性，在该框架中，单个组件预期性能的可变性会导致失败过程异质和不确定。我们使用贝叶斯方法将失败模型拟合到模拟中生成的故障数据。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的差异来测试算法。我们评估了每个模型如何近似单个组件的故障属性以及整体系统的故障属性。最后，我们定义了一种升级政策，以实现有针对性的降低风险敞口，并比较每个模型建议的升级幅度。
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.
