  The operating point of a power system may change due to slow enough
variations of the power injections. Rotating machines in the bulk system can
absorb smooth changes in the dynamic states of the system. In this context, we
present a novel reservoir computing (RC) method for estimating power system
quasi-steady states. By exploiting the behavior of an RC-based recurrent neural
network, the proposed method can capture the inherent nonlinearities in the
power flow equations. Our approach is compared with traditional methods,
including least squares, Kalman filtering, and particle filtering. We
demonstrate the estimation performance for all the methods under normal
operation and sudden load change. Extensive experiments tested on the standard
IEEE 14-bus and 300-bus cases corroborate the merit of the proposed approach.
