用改进的遗传算法对活跃的配电网络进行电力恢复优化策略.
Pengpeng Lyu1, Qiangsheng Bu1, Yu Liu2
1State Grid Jiangsu Electric Power Co., Ltd. Research Institute, Nanjing 211103, China.
Biomimetics (Basel, Switzerland)
|September 26, 2025
概括
本研究介绍了一种电力恢复策略,该策略最大限度地利用局部资源在电网中断期间最大限度地提供关键负载. 新的自适应多点交叉遗传算法 (AMCGA) 提高了系统的可靠性和稳定性.
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:
- 配电网络供应器中断需要对关键负载进行可靠的电力恢复.
- 当主网支持不可用时,像光伏系统 (PVs) 这样的本地化资源提供了解决方案.
- 现有的战略可能没有充分优先考虑在岛屿运营期间的关键负载恢复.
研究的目的:
- 开发和验证一项恢复电源的策略,最大限度地提高恢复关键负载的时间.
- 通过在停电期间优先考虑基本服务来提高电力系统的可靠性.
- 优化岛屿分区以提高稳定性和减少恢复波动.
主要方法:
- 使用宽度优先搜索 (BFS) 进行负载计数.
- 使用长短期记忆 (LSTM) 神经网络进行微网生成预测.
- 适应性多点交叉遗传算法 (AMCGA) 优化岛屿分区.
主要成果:
- 与传统的遗传算法相比,AMCGA在融合速度上有42.5%的改进.
- 拟议的战略大大延长了关键负载恢复时间.
- 在关键负载恢复,岛屿分区和减少恢复波动方面观察到卓越的性能.
结论:
- 基于AMCGA的电源恢复策略有效地最大限度地提高了在料中断期间的关键负载供应.
- 这种方法提高了电力系统的整体可靠性和稳定性.
- 该战略为弹性发电网提供了一个强大的解决方案.
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