  Distribution system state estimation (DSSE) is paramount for effective state
monitoring and control. However, stochastic outputs of renewables and
asynchronous streaming of multi-rate measurements in practical systems largely
degrade the estimation performance. This paper proposes a deep reinforcement
learning (DRL)-enabled adaptive DSSE algorithm in unbalanced distribution
systems, which tackles hybrid measurements with different time scales
efficiently. We construct a three-step forecasting-aided state estimation
framework, including DRL-based parameter identification, prediction, and state
estimation, with multi-rate measurements incorporating limited synchrophasor
data. Furthermore, a DRL-based adaptive parameter identification mechanism is
embedded in the prediction step. As a novel attempt at utilizing DRL to enable
DSSE adaptive to varying operating conditions, this method improves the
prediction performance and further facilitates accurate state estimation. Case
studies in two unbalanced feeders indicate that our method captures state
variation with multi-source multi-rate data efficiently, outperforming the
traditional methods.
