一个基于微电网保护剂的故障分类方案与参数冲击分析
Saman Esmaeilbeigi1, Hossein Kazemi Karegar2, Abolfazl Najar3
1Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran. s_esmaeilbeigi@sbu.ac.ir.
Scientific reports
|December 22, 2025
概括
本研究介绍了一种基于保护剂 (PA) 的方法,使用混合深度神经网络 (DNN) 进行微电网故障检测和定位 (FDL). 该研究调查了各种参数如何影响FDL准确性,为改进微电网保护策略提供了见解.
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:
- 微电网保护在故障识别方面面临着挑战.
- 过电流保护是常见的,但深度神经网络 (DNN) 提供了更高的准确性.
- 基于深度学习的故障检测和定位 (FDL) 方案可能对参数变化敏感.
研究的目的:
- 提出基于保护剂 (PA) 的故障分类方法,使用智能电子设备 (IED) 和微电网的混合DNN.
- 调查各种参数对基于DNN的FDL系统性能的影响.
- 对拟议的FDL计划进行全面的敏感性分析.
主要方法:
- 开发了一个FDL方案,使用两种类型的DNN (单层和混合层) 和PA.
- 分析了基于超电流的保护场景和参数影响.
- 对微电网拓,IED,DNN结构,保护方案和数据传输等因素进行了数据和灵敏度分析.
主要成果:
- 拟议的算法实现了高精度,故障检测率从95.54%到99.96%不等.
- 故障类型/相位检测精度在95.56%至99.86%之间,故障位置错误在1.27%至6.51%之间.
- 混合DNN (DNN-2) 始终优于单层DNN (DNN-1),尽管观察到参数灵敏度.
结论:
- 基于PA的混合DNN方法提高了微电网故障分类和位置准确性.
- 参数选择和微电网配置显著影响基于DNN的保护方案的性能.
- 这项研究为利用智能方法优化微电网保护提供了宝贵的见解.
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