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Radial System Protection

Radial System Protection

Radial systems employ time-delay overcurrent relays to reduce load interruptions. When a fault occurs, the nearest breaker opens first, while upstream breakers remain closed due to longer delay settings. This approach ensures minimal disruption to the rest of the system.
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Related Experiment Video

Updated: Aug 16, 2025

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

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Published on: February 1, 2020

Parallel Radars: From Digital Twins to Digital Intelligence for Smart Radar Systems.

Yuhang Liu^1,2, Yu Shen^1,2, Lili Fan³

¹The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

Sensors (Basel, Switzerland)

|December 23, 2022

Summary

Smart radar systems are developed using Parallel Radars within cyber-physical-social systems (CPSS). This framework integrates human factors for intelligent, real-time processing in applications like autonomous driving.

Keywords:

ACP method cyber-physical-social systems (CPSS)federated radars parallel radars

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Last Updated: Aug 16, 2025

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Published on: February 12, 2014

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Published on: May 1, 2018

Area of Science:

Cyber-Physical-Social Systems (CPSS)
Artificial Intelligence
Radar Technology

Background:

Current radar systems act as simple data collectors, insufficient for complex, real-time intelligent processing.
Digital twins in cyber-physical systems (CPS) lack human factor integration, limiting smart radar development.
Human involvement is critical for radar operation and management, necessitating a new framework.

Purpose of the Study:

To propose a novel framework, Parallel Radars, for developing smart radar systems.
To address the limitations of existing CPS digital twins by incorporating human factors.
To enhance radar capabilities for real-time, intelligent information processing in complex environments.

Main Methods:

Utilizing ACP-based parallel intelligence within CPSS to construct the Parallel Radars framework.
Developing a three-part system: Descriptive Radar, Predictive Radar, and Prescriptive Radar.
Proposing federated radars to manage data silos and ensure data privacy.

Main Results:

Demonstrated the effectiveness of Parallel Radars through experiments, using mine detection in autonomous driving as a case study.
Showcased the ability of the framework to integrate human factors for enhanced radar intelligence.
Validated the parallel execution capabilities of Descriptive, Predictive, and Prescriptive Radars.

Conclusions:

Parallel Radars offer a robust framework for creating intelligent radar systems by integrating human factors within CPSS.
The proposed federated radar approach effectively addresses data privacy and connectivity challenges.
This novel approach significantly advances radar capabilities for demanding applications like autonomous driving.