Entries for Data-Driven Distributed (3D) Solar Visibility Prize

The team extends the practically validated robust distribution state estimator for changing system topology and operational conditions.

Distributed system predictions that take into consideration the Physics that drive the system and as well as historic data.

Estimate the three-phase system state accounting for topology and measurement errors using analytical and statistical methods.

DSSE using ML algorithm

SE+ uses L0-based objective function for accurate, robust distribution system state estimation, handling bad data effectively.

A ML-based bad data and topology change detector will provide reliable inputs for another ML DSSE algorithm to estimate the system states.

ML-based approach to distribution system state estimation.

We plan to use Deep Learning, discrete event simulation, or Time Series Analysis, to predict state vectors.

I intend to use machine learning techniques to solve this challenge.

Combining physics based approaches with Machine Learning based approaches