Unscented Trainable Kalman Filter Based on Deep Learning Method Considering Incomplete Information-Reference-Cited by-同舟云学术

Unscented Trainable Kalman Filter Based on Deep Learning Method Considering Incomplete Information

Published:2023 Issue: Volume:11 Page:50700-50709
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Yu Yanjie¹^ORCID,Li Qiang¹^ORCID,Zhang Houyi²

Affiliation:

1. State Key Laboratory of Power Transmission Equipment and System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing, China

2. Electric Power Research Institute, Guizhou Power Grid Company Ltd., Guiyang, China

Funder

China Southern Power Grid through the research project ‘Research on reliable operation of rural microgrids based on multiple data sources’

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10129202.pdf?arnumber=10129202

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5. Improved dynamic state estimation of power system using unscented Kalman filter with more accurate prediction model

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