Log In Sign Up

Time Synchronized State Estimation for Incompletely Observed Distribution Systems Using Deep Learning Considering Realistic Measurement Noise

by   Behrouz Azimian, et al.

Time-synchronized state estimation is a challenge for distribution systems because of limited real-time observability. This paper addresses this challenge by formulating a deep learning (DL)-based approach to perform unbalanced three-phase distribution system state estimation (DSSE). Initially, a data-driven approach for judicious measurement selection to facilitate reliable state estimation is provided. Then, a deep neural network (DNN) is trained to implement DNN-based DSSE for systems that are incompletely observed by synchrophasor measurement devices (SMDs). Robustness of the proposed methodology is demonstrated by considering realistic measurement error models for SMDs. A comparative study of the DNN-based DSSE with classical linear state estimation (LSE) indicates that the DL-based approach gives better accuracy with a significantly smaller number of SMDs


page 1

page 4

page 5


State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks

Time-synchronized state estimation for reconfigurable distribution netwo...

High-Speed State Estimation in Power Systems with Extreme Unobservability Using Machine Learning

Fast timescale state estimation for a large power system can be challeng...

Physics-Informed Deep Neural Network Method for Limited Observability State Estimation

The precise knowledge regarding the state of the power grid is important...

A Review and Refinement of Surprise Adequacy

Surprise Adequacy (SA) is one of the emerging and most promising adequac...

Data-driven simulation for general purpose multibody dynamics using deep neural networks

In this paper, a machine learning-based simulation framework of general-...

Enabling Trust in Deep Learning Models: A Digital Forensics Case Study

Today, the volume of evidence collected per case is growing exponentiall...