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Maximum Power Flow and Line Loadability

Maximum Power Flow and Line Loadability

The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.

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相关实验视频

Updated: Apr 12, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

使用半监督学习算法预测电力线通信节点的可用性.

Kareem Moussa^1,2, Khaled Mostafa Elsayed^3,4, M Saeed Darweesh^5,6

¹University of Science and Technology, Zewail City, Giza, 12578, Egypt. p-kareem.moussa@zewailcity.edu.eg.

Scientific reports

|May 21, 2025

概括

这项研究提高了使用自训练机器学习的电源线通信 (PLC) 节点可用性预测. 标签扩散实现了94.67%的准确性,优化了数据传输效率.

关键词:

标签传播标签传播标签的传播标签的传播轻度梯度增强机器 (LGBM) 是一个机器学习机器学习电力线路通讯 (PLC) 系统自学训练分类器自学训练分类器半监督学习半监督学习支持矢量机器 (SVM) 是一个支持矢量机器.

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Last Updated: Apr 12, 2026

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科学领域:

电气工程电气工程
计算机科学计算机科学
数据科学数据科学数据科学

背景情况:

电力线通信 (PLC) 网络在向不可用节点传输数据方面面临挑战,导致延迟.
机器学习通过预测节点可用性来提供解决方案,提高网络效率.

研究的目的:

调查自训练机器学习算法的有效性,以预测PLC网络中的节点可用性.
为了比较各种自我训练和监督学习模型的表现.

主要方法:

从500节点的PLC网络中收集了2000个实例的数据集,其中包括CINR,SNR和RSSI.
对LGBM,SVM,标签传播和标签传播算法进行了自我训练.
监督学习模型 (随机森林,逻辑回归) 用于比较.

主要成果:

标签扩散表现出卓越的性能,准确度为94.67%,f1得分为0.947,精度为0.946,回忆率为0.947.
最好的模型以最小的训练时间 (0.018秒) 和内存消耗 (0.99MB) 实现了这一目标.

结论:

自训练算法,特别是标签扩散,对于预测PLC网络中的节点可用性非常有效.
优化节点可用性预测显著减少数据传输延迟,并改善整体网络性能.