Asynchronous Hierarchical Forecasting-Aided State Estimator With Sub-Area Data Validation for Power Systems

Estimating the states of a large interconnected power system for monitoring the network, considering its time evolution, is a computationally complex task owing to its large geographical spread. An asynchronous hierarchical forecasting-aided state estimator (AHFASE), which collates the estimates, evaluated by smaller reduced order subsystems to compute the global estimates is proposed in this paper. Individual subsystems process their own local observations in parallel to evaluate the local estimates; thereby reducing the overall computational complexity. Local information from each subsystem is then transmitted to the global estimator, where the incoming information is assimilated to evaluate a near-optimal estimate. The local processors operate asynchronously, transmitting information to the central processor as and when the local observations are processed. The local information arriving at the central processor could become corrupted due to error in communication network or due to some malicious attacks. An asynchronous data validation algorithm is also developed to validate the accuracy of the incoming information. A multi-level architecture of AHFASE is also discussed. The proposed method is computationally efficient, robust to bad data, and resilient to central estimator failure. The accuracy of the developed algorithm is assessed using IEEE 30 and 118-bus test beds.