基于机器学习的多代理平台的直流网络智能保护方案
Saman Esmaeilbeigi1, Hossein Kazemi Karegar2, Amirhossein Akbarisharif3
1Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran. s_esmaeilbeigi@sbu.ac.ir.
Scientific reports
|September 26, 2025
概括
本研究介绍了一种新型的多代理人工智能系统,用于保护DC微电网 (DCMGs). 先进的方案精确地分类和定位故障,提高电网稳定性和安全性.
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:
- 直流微电网 (DCMGs) 的集成正在增加,由于基于逆变器的发电,这带来了独特的保护挑战.
- 传统的保护方法对于DCMG的独特故障电流特性是不够的.
研究的目的:
- 为直流微电网 (DCMGs) 提出一种基于多种代理的新型保护方案.
- 开发和评估三种不同的人工智能驱动的故障分类和定位方法.
主要方法:
- 设计了一个包含设备,变电站和系统层的多代理系统.
- 机器学习算法 (SVM,DT) 和深度神经网络 (DNN) 用于故障分类和定位.
- 模拟使用DIGSILENT,MATLAB和Python (TensorFlow/Keras) 进行.
主要成果:
- 拟议方案在故障检测和定位方面表现出高效率.
- 这三种故障分类方法实现了精确的故障识别和精确的故障位置.
- 深度神经网络显示了对故障分类的增强训练能力.
结论:
- 开发的多剂保护方案对DCMGs有效.
- 基于AI的故障检测,分类和定位方法是可靠和准确的.
- 这项研究证实了人工智能在保护现代电力系统方面的潜力.
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