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基于图形神经网络的变电站中定位二次电路故障的方法
Ziping Peng1, Gang Chen1, Junjie Zhang1
1Guangdong Power Grid Limited Liability Company Jiangmen Power Supply Bureau, China.
Heliyon
|December 5, 2024
概括
一种新的图形神经网络方法准确地定位智能变电站二次电路中的故障. 这种方法提高了故障检测准确度和系统稳定性,从而提高了电网可靠性.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 智能变电站依赖于二级电路进行操作控制和监控.
- 这些电路的准确故障位置对于保持电网稳定性和防止级联故障至关重要.
研究的目的:
- 开发用于智能变电站二次电路的准确和便携式故障定位方法.
- 利用图形神经网络来增强故障检测能力.
主要方法:
- 构建了二次电路的图形数据库模型来表示设备互连.
- 定义故障位置作为图形分类问题,使用新型故障图形生成模型.
- 开发了一个基于图形神经网络 (GNN) 的故障定位模型,并优化了二进制交叉.
主要成果:
- 与现有模型相比,拟议的GNN方法显示出更高的故障位置精度.
- 实验表明,在识别各种网络拓和配置中的故障方面,稳定性得到了提高.
- 对220千伏智能变电站的案例研究验证了该方法的有效性.
结论:
- 图形神经网络方法为智能变电站的二次电路故障位置提供了显著的进步.
- 这种方法为提高电网可靠性提供了强大而准确的解决方案.
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