Massive integration of renewables and electric vehicles comes with unknown dynamics - what exemplifies the need for fast, accurate, and robust distribution system state estimation (DSSE). Due to limited real-time measurements however, optimization-oriented DSSE faces major challenges related to convergence, as well as multiple global/local minima. To address these challenges, this paper puts forth a novel deep neural network (DNN)-based computational framework for DSSE that consists of two modules: a deep recurrent neural network (RNN) based pseudo-measurement postulating module, and a prox-linear net-based real-time state estimation module. Both RNN and prox-linear nets learn complex nonlinear functions, and can afford efficient training by leveraging existing deep learning platforms. Numerical tests with semi-real load data demonstrate the merits of the DNN-based DSSE approach.
|Original language||English (US)|
|Title of host publication||2019 IEEE Data Science Workshop, DSW 2019 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - Jun 2019|
|Event||2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States|
Duration: Jun 2 2019 → Jun 5 2019
|Name||2019 IEEE Data Science Workshop, DSW 2019 - Proceedings|
|Conference||2019 IEEE Data Science Workshop, DSW 2019|
|Period||6/2/19 → 6/5/19|
Bibliographical noteFunding Information:
This work was supported in part by NSF grants 1508993, 1509040, and 1711471.
© 2019 IEEE.
- Distribution system state estimation
- deep neural network
- pseudo measurement
- recurrent neural network