Data-Driven Distributed Kalman Filter-Based Sensor Fault Isolation and Estimation for Large-Scale Interconnected Systems
针对大规模互联动态系统，提出一种数据驱动的分布式卡尔曼滤波器方案，利用局部和邻居数据解耦未知交互，实现子系统与元件级的传感器故障隔离与估计，并通过电力网络案例验证效果。
This article proposes a data-driven distributed Kalman filter (DKF)-based sensor fault isolation and estimation scheme for large-scale interconnected dynamic systems, composed of heterogeneous subsystems coupled through a directed topological graph. A local diagnosis unit (LDU) is established for each subsystem, where the data-driven DKF-based residual generator is constructed using local and neighboring process data, effectively decoupling the totally unknown interaction component. Subsequently, fully distributed sensor fault isolation is realized at the subsystem and element levels in simultaneous-fault cases. Both local and neighboring sensor fault isolation can be realized in the LDU, allowing the global system sensor fault isolation with only several key LDUs. Then, the data-driven DKF-based estimator is built in each LDU to estimate sensor faults occurring in multiple subsystems. The distributed Kalman gain is computed in a fully distributed manner, with stability analysis performed locally without overall system knowledge. Finally, the effectiveness and performance of the proposed scheme are validated through case studies on the power network system.