Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: State and Topology Estimation for Unobservable Distribution Systems
  using Deep Neural Networks   Time-synchronized state estimation for reconfigurable distribution networks
is challenging because of limited real-time observability. This paper addresses
this challenge by formulating a deep learning (DL)-based approach for topology
identification (TI) and unbalanced three-phase distribution system state
estimation (DSSE). Two deep neural networks (DNNs) are trained for
time-synchronized DNN-based TI and DSSE, respectively, for systems that are
incompletely observed by synchrophasor measurement devices (SMDs) in real-time.
A data-driven approach for judicious SMD placement to facilitate reliable TI
and DSSE is also provided. Robustness of the proposed methodology is
demonstrated by considering non-Gaussian noise in the SMD measurements. A
comparison of the DNN-based DSSE with more conventional approaches indicates
that the DL-based approach gives better accuracy with smaller number of SMDs.

9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph