Transformer网络技术在电池储能管理中的应用与优化
Application and optimization of Transformer network technology in battery energy storage management
徐霞1
作者信息
- 1. 长江职业学院数据信息学院,湖北 武汉 430074
- 折叠
摘要
电池储能管理系统在现代能源网络中起关键性的作用.随着对能源效率和储能系统可靠性的要求日益提升,深度学习技术在电池储能管理中的应用受到了广泛关注.本文旨在探讨Transformer网络技术在电池储能管理中的创新应用与优化策略.首先,本文介绍了电池储能管理系统的基本概念和现阶段面临的主要挑战.然后全面分析了深度学习技术在电池储能管理中的应用现状,着重讨论了各种网络模型的性能及其在实际应用中的成效,并提出了一种基于Transformer架构优化的电池储能管理策略,该方法在提升系统稳定性方面的显著优势.本文的研究不仅为电池储能管理提供了新的技术手段,也为未来相关技术的进一步发展提供了理论支持和实践参考.
Abstract
Battery energy storage management systems play a key role in modern energy networks.With the increasing requirements for energy efficiency and reliability of energy storage systems,the application of deep learning techniques in battery energy storage management has received widespread attention.The purpose of this paper is to discuss the innovative application and optimisation strategy of Transformer network technology in battery energy storage management.Firstly,this paper introduces the basic concept of battery energy storage management system and the main challenges faced at this stage.Then,it comprehensively analyses the current status of the application of deep learning technology in battery energy storage management,focusing on the performance of various network models and their effectiveness in practical applications.Finally,a battery energy storage management strategy based on the optimisation of Transformer architecture is proposed,which has significant advantages in enhancing system stability.The research in this paper not only provides new technical means for battery energy storage management,but also provides theoretical support and practical reference for the further development of related technologies in the future.
关键词
Transformer网络技术/电池储能管理/深度学习Key words
Transformer network technology/battery energy storage management/deep learning引用本文复制引用
基金项目
湖北省教育科学规划课题(2023GB215)
出版年
2024
国家科技期刊平台
NSTL
万方数据