Multi‐objective auto‐encoder deep learning‐based stack switching scheme for improved battery life using error prediction of wind‐battery storage microgrid-Reference-Cited by-同舟云学术

Multi‐objective auto‐encoder deep learning‐based stack switching scheme for improved battery life using error prediction of wind‐battery storage microgrid

Published:2021-08-12 Issue:14 Volume:45 Page:20331-20355
ISSN:0363-907X
Container-title:International Journal of Energy Research
language:en
Short-container-title:Int J Energy Res

Author:

Mishra Sthita Prajna¹,Krishna Rayi Vijaya¹,Dash Pradipta Kishore²^ORCID,Bisoi Ranjeeta³

Affiliation:

1. Department of EEE GMR Institute of Technology Rajam Andhra Pradesh India

2. Department of Electrical and Electronics Engineering Siksha O Anusandhan University Bhubaneswar Odisha India

3. Multidisciplinary Research Cell Siksha O Anusandhan Deemed to be University Bhubaneswar Odisha India

Publisher

Wiley

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/er.7117

Reference45 articles.

1. Control Strategies for Battery Energy Storage for Wind Farm Dispatching

2. LiX DongH LiW XiaokangL.Control strategy of battery state of charge for wind/battery hybrid power system. IEEE International Symposium on Industrial Electronics (ISIE); 2010. 2723‐2726.

3. Optimal Energy Management of Wind-Battery Hybrid Power System With Two-Scale Dynamic Programming

4. Cooperative Optimal Control of Battery Energy Storage System Under Wind Uncertainties in a Microgrid

5. Centralized Control for Parallel Operation of Distributed Generation Inverters in Microgrids

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