An Asynchronous Decentralized Forecasting-Aided State Estimator for Power Systems

Tracking the system states of an extensive power system is an onerous task as it estimates the power system states by processing a large measurement set. This paper proposes a new asynchronous decentralized forecasting-aided state estimator (ADFASE) to track the states of a power system. The networked power system is partitioned into smaller systems, which processes the local measurements in parallel to estimate system states of reduced dimensionality; thereby reducing the computational complexity. The subsystems then communicate the local information to neighboring subsystems, which collate the incoming information to obtain near-optimal estimates. The decentralized topology eliminates the need for any centralized infrastructure. Each processor operates in an asynchronous manner, and the information is communicated to neighboring subsystems as and when the information is processed. The information received by the subsystems are assimilated asynchronously and does not need any prior synchronization between the nodes. The performance of the proposed estimator is evaluated using IEEE 30 and 118-bus systems.