  Power system state estimation (PSSE) is commonly formulated as weighted
least-square (WLS) algorithm and solved using iterative methods such as
Gauss-Newton methods. However, iterative methods have become more sensitive to
system operating conditions than ever before due to the deployment of
intermittent renewable energy sources, low carbon technologies (e.g., electric
vehicles), and demand response programs. Appropriate PSSE approaches are
required to avoid pitfalls of the WLS-based PSSE computations for accurate
prediction of operating conditions. This paper proposes a data-driven real-time
PSSE using a deep ensemble learning algorithm. In the proposed approach, the
ensemble learning setup is formulated with dense residual neural networks as
base-learners and multivariate-linear regressor as meta-learner. Historical
measurements and states are utilised to train and test the model. The trained
model can be used in real-time to estimate power system states (voltage
magnitudes and phase angles) using real-time measurements. Most of current
data-driven PSSE methods assume the availability of a complete set of
measurements, which may not be the case in real power system data-acquisition.
This paper adopts multivariate linear regression to forecast system states for
instants of missing measurements to assist the proposed PSSE technique. Case
studies are performed on various IEEE standard benchmark systems to validate
the proposed approach. The results show that the proposed approach outperforms
existing data-driven PSSE methods techniques.
