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

被引：2

作者：

Azimian, B. ^{[1
]}

Sen Biswas, R. ^{[1
]}

Pal, A. ^{[1
]}

Tong, Lang ^{[2
]}

机构：

[1] Arizona State Univ, Sch Elect Comp & Energy Engn, Tempe, AZ 85287 USA

[2] Cornell Univ, Sch Elect & Comp Engn, Ithaca, NY 14850 USA

来源：

2021 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM) | 2021年

关键词：

Deep neural network (DNN); Gaussian mixture model (GAM); State estimation; Synchrophasor measurements;

D O I：

10.1109/PESGM46819.2021.9637858

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

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 perform 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 indicates that the DL-based approach gives better accuracy with a significantly smaller number of SMDs.

引用

页数：5