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Cable Subjected to a Distributed Load

Cable Subjected to a Distributed Load

The analysis of suspension bridges is a complex and critical process that involves multiple factors, including the shape and tension of the main cables. The main cables of suspension bridges are subjected to distributed loads, which result in changes in tensile forces and deformation of the cable. These loads must be carefully considered to ensure that the bridge is safe and capable of supporting the weight of different loads.

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...

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A coaxial cable consists of a central copper conductor used for transmitting signals, followed by an insulator shield, a metallic braided mesh that prevents signal interference, and a plastic layer that encases the entire assembly.
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Cable Subjected to Concentrated Loads

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Flexible cables are commonly used in various applications for support and load transmission. Consider a cable fixed at two points and subjected to multiple vertically concentrated loads. Determine the shape of the cable and the tension in each portion of the cable, given the horizontal distances between the loads and supports.

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Related Experiment Video

Updated: Mar 19, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Cable online partial discharge detection and state evaluation based on deep belief network and swarm intelligence

Yucheng Hou^1,2, Yifei Li^3,4, Baoming Song⁴

¹Beijing Dingcheng Hong'an Technology Development Co., Ltd., Beijing, 100075, China. YuchengHVV@163.com.

Scientific Reports

|March 18, 2026

Summary

This summary is machine-generated.

This study introduces an advanced framework for detecting partial discharge (PD) in urban power grid cables. The new method enhances accuracy and real-time monitoring for safer grid operations.

Keywords:

Deep belief network Elastic weight consolidation technique Krill-Herd algorithm Online status assessment Partial discharge detection

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Published on: December 15, 2023

Area of Science:

Electrical Engineering
Power Systems
Artificial Intelligence

Background:

Urban power grids increasingly rely on high-voltage cable lines for transmission.
Insulation degradation due to partial discharge (PD) poses a significant risk to grid reliability.
Existing PD detection methods face limitations in sensitivity, real-time performance, and scalability.

Purpose of the Study:

To develop an efficient and reliable online monitoring framework for cable PD detection and state assessment.
To improve the accuracy, real-time performance, and state evaluation capabilities of PD detection systems.
To provide robust technical support for the safe operation of urban power grid cable assets.

Main Methods:

Integration of an improved Deep Belief Network (DBN) with an Inertial Krill-Herd Algorithm with Differential Bat Adaptation (IKHA-DBA).
Enhancement of the DBN model using DropConnect and elastic weight consolidation techniques.
Optimization of the DBN using the IKHA-DBA algorithm for superior PD detection.

Main Results:

The improved DBN model demonstrated significant gains in precision, recall, and AUC compared to ResNet50-SE on the B0005 dataset.
The final IKHA-DBA optimized DBN model further improved detection accuracy and robustness on both B0005 and B0006 datasets.
The proposed framework achieved comprehensive improvements in accuracy, robustness, and real-time performance for cable PD online detection.

Conclusions:

The developed framework offers a reliable solution for online partial discharge detection in urban power grid cables.
The integration of advanced AI algorithms significantly enhances the capabilities of existing PD monitoring technologies.
This research provides crucial technical support for ensuring the safe and stable operation of critical power infrastructure.