相关实验视频
用贝叶斯优化LSTM用于分配网络的净负载预测的故障恢复策略.
1College of Artificial Intelligence and Automation, Hohai University, Nanjing 210024, China.
Entropy (Basel, Switzerland)
|September 27, 2025
概括
这项研究通过使用贝叶斯优化的LSTM神经网络预测净负载来增强电网故障恢复. 拟议的战略改善了关键负载恢复,并减少了中断期间的网络损失.
科学领域:
- 电气工程 电气工程
- 电力系统 电力系统
- 人工智能的人工智能
背景情况:
- 分布式能源资源 (DER) 波动性使传统配电网络故障修复复复杂化.
- 准确的净负载预测对于有效的恢复策略至关重要.
研究的目的:
- 开发一个先进的故障修复策略,考虑分布网络的DER波动性.
- 为了提高故障后电力恢复的速度和效率.
主要方法:
- 利用贝叶斯优化的长短期记忆 (LSTM) 神经网络进行精确的净负载预测.
- 实施快速恢复优化模型,目标是恢复关键负载,尽量减少切换和减少网络损失.
- 采用混合基因算法-量子粒子群集优化 (GA-QPSO) 来解决优化模型.
主要成果:
- 在净负载预测中实现了0.9569的R2和12.15kW的RMSE.
- 与现有方法相比,减少了33.2%的网络损失.
- 电源持续时间从60分钟延长到120分钟,负载恢复率从72.7%提高到75.8%.
结论:
- 拟议的净负载预测和恢复战略有效地解决了DER波动性的影响.
- 该GA-QPSO优化显著提高恢复的准确性和效率.
- 在减少网络损失,供应持续时间和负载恢复能力方面取得了明显的改进.
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