A Forecasting-Aided State Estimator Based on Improved Bayesian Deep LSTM Neural Network-Reference-Cited by-同舟云学术

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A Forecasting-Aided State Estimator Based on Improved Bayesian Deep LSTM Neural Network

Published:2023-09-21 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Power System Technology (PowerCon)
language:
Short-container-title:

Author:

Wang Yue¹,Yu Yue¹,Jin Zhaoyang¹,Ding Lei¹,Terzija Vladimir²,Cetenovic Dragan³

Affiliation:

1. School of Electrical Engineering, Shandong University,Jinan,China

2. School of Electrical and Electronic Engineering, New Castle University,Newcastle,United Kingdom

3. School of Electrical and Electronic Engineering, University of Manchester,Manchester,United Kingdom

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10330799/10330806/10330986.pdf?arnumber=10330986

Reference13 articles.

1. Usage of Artificial Neural Networks for pseudo measurement modeling in low voltage distribution systems

2. A Hybrid Deep Learning-Based Power System State Forecasting

3. Dynamic State Estimation of New Energy Power Systems Considering Multi-Level False Data Identification Based on LSTM-CNN

4. Fault Diagnosis of Generator Bearings Based on Bayesian Neural Network with Stochastic Variational Inference;Jinhua;Control and Decision,2023

5. Using Bayesian Deep Learning to Capture Uncertainty for Residential Net Load Forecasting

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