基于LSTM的增强机器人代理用于在低压分布式光伏发电分配网络中的负载预测
Xudong Zhang1, Junlong Wang1, Jun Wang2
1State Grid Hebei Electric Power Company, Shijiazhuang, China.
Frontiers in neurorobotics
|July 26, 2024
概括
本研究介绍了一种改进的LSTM模型,用于准确预测低压光伏发电网的负载. 机器人辅助的方法提高了电网稳定性的电压预测准确性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 可再生能源系统可再生能源系统
背景情况:
- 低压分布式光伏 (PV) 发电网 (PDN) 的安全运行需要准确的负载预测.
- 现有的预测方法可能无法完全捕捉光伏PDN中的复杂电压变化.
研究的目的:
- 为低压分布式光伏PDN开发机器人辅助负载预测方法.
- 为了提高电压预测的准确性,以改善电网管理和稳定性.
主要方法:
- 提出了一种增强的长短期记忆 (LSTM) 网络,与频域分解 (FDD) 集成.
- 利用FDD识别边界点并将信号分解为低频和高频组件.
- 采用LSTM来单独预测频率组件,并采用密集层来提取特征.
主要成果:
- 与反复神经网络 (RNN) 和支持矢量机器 (SVM) 模型相比,拟议的FDD+LSTM模型显示出更高的预测准确性.
- 通过不同的季节和时间尺度 (1小时和4小时) 实现了精确的电压预测.
- 该模型有效地捕获了各种频率组件的电压变化模式.
结论:
- 增强的LSTM模型为在低压分布式光伏PDN中准确的负载预测提供了强大的解决方案.
- 准确的电压预测对于这些网络的安全运行和调度控制至关重要.
- 这些发现支持PDN技术和相关产业链的进步.
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