论文标题
多个时间尺度的插定辅助状态估计分配系统
Multi Time-scale Imputation aided State Estimation in Distribution System
论文作者
论文摘要
随着向智能电网的过渡，我们目睹了传感器部署和分配系统中智能计量基础架构的显着增长。但是，来自这些传感器和仪表的信息通常在不同的时间尺度上进行不均匀采样，并且不完整。有效地汇总这些信息源以实现情境意识至关重要。为了调和异质的多尺度时间序列数据，我们提出了一个多任务高斯流程框架。该框架利用了时间序列数据之间的时空相关性，以在任何所需的时间尺度上将数据归为数据，同时在归纳上提供置信度界限。通过基于矩阵完成的状态估计策略来说明估算数据对分配系统操作的价值。 IEEE 37总线分布系统的结果揭示了所提出的方法相对于线性插值方法的出色性能。
With the transition to a smart grid, we are witnessing a significant growth in sensor deployments and smart metering infrastructure in the distribution system. However, information from these sensors and meters are typically unevenly sampled at different time-scales and are incomplete. It is critical to effectively aggregate these information sources for situational awareness. In order to reconcile the heterogeneous multi-scale time-series data, we present a multi-task Gaussian process framework. This framework exploits the spatio-temporal correlation across the time-series data to impute data at any desired time-scale while providing confidence bounds on the imputations. The value of the imputed data for distribution system operation is illustrated via a matrix completion based state estimation strategy. Results on the IEEE 37 bus distribution system reveals the superior performance of the proposed approach relative to linear interpolation approaches.
