在物联网混合电网系统中使用基于人群感应和微气候数据的深度学习算法进行电池管理
Srinivasan Rajamani1, Arulmozhiyal Ramasamy2
1Department of Electrical and Electronics Engineering, Anna University, Chennai, Tamilnadu, India. pavisshsrini@gmail.com.
Scientific reports
|July 6, 2025
概括
本研究介绍了一种支持物联网的混合电网系统 (HGS),使用深度学习来进行住宅电力管理. 该系统优化了能源,减少了电网需求,并提高了国内负载的稳定性.
科学领域:
- 电气工程 电气工程
- 可再生能源系统可再生能源系统
- 人工智能在能源中的作用
背景情况:
- 住宅混合电网系统 (HGS) 面临的挑战是管理不同的国内负载,使用不同的工作周期.
- 集成可再生能源,如光伏 (PV) 和风力发电系统 (PWS) 需要智能控制稳定性和效率.
- 现有系统在电网互动,能源出口和最佳电池/超级电容器管理方面扎.
研究的目的:
- 为住宅HGS提出一个物联网 (IoT) 支持的PWS (IPWS).
- 实施先进的深度学习算法,以优化能量流,电网交互和稳定性.
- 评估不同深度学习控制器和储能配置的性能.
主要方法:
- 开发了一个集成光伏,风能,酸电池和超级电容器的IPWS,具有物联网功能.
- 使用人群传感来获取微气候数据,以调整零出口转换器和电池管理系统 (BMS).
- 实现并比较了控制器和BMS的混合深度学习算法 (SCO-LSTM,JO-LSTM,HBO-LSTM).
主要成果:
- 带有零出口转换器的IPWS减少了电网上的电力需求,并将多余的能量储存在超级电容器中.
- 基于JO-LSTM/HBO-LSTM的BMS的IPWS有效地消除了输出功率波动,增强了短暂稳定性 (TS) 和减压比率 (DR).
- 使用JO-LSTM控制器和超级电容器的IPWS提高了29%的功率系数,减少了14%的波,增加了6%的DR,并实现了住宅负载的低TS.
结论:
- 拟议的IPWS提供了一个强大的解决方案,用于管理住宅HGS的国内负载.
- 深度学习控制器显著提高了系统稳定性,电源质量和能源效率.
- 物联网和先进算法的集成为家庭的可再生能源管理提供了可扩展和智能化的方法.
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