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SMART TRACTION POWER SUPPLY SYSTEMS FOR HIGH-SPEED RAILWAYS.

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Given the increasingly busy railway networks, increased axle loads, and railway speeds,modern railways and urban transport systems require smart and energy-efficient tractionpower supply products and solutions to ensure safe, reliable, and environmentallysustainable operations.

Bibliographic Details
Main Author: GAO, SHIBIN
Corporate Author: ScienceDirect (Online service)
Format: eBook
Language:English
Published: [S.l.] : ELSEVIER, 2025.
Subjects:
High speed trains.
Electric railroads > Power supply.
Railroads > Electrification.
Trains à grande vitesse.
Chemins de fer électriques > Alimentation en énergie.
Chemins de fer > Électrification.
high-speed trains.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • Smart Traction Power Supply Systems for High-Speed Railways
  • Copyright Page
  • Contents
  • Preface
  • 1 Introduction
  • 1.1 Intelligence
  • 1.1.1 Human intelligence
  • 1.1.2 Artificial intelligence
  • 1.1.3 Smart machine
  • 1.1.4 Smart system
  • 1.2 Smart railways
  • 1.2.1 Development of foreign smart railways
  • 1.2.1.1 Background to Shift2Rail
  • 1.2.1.1.1 Comprehensive challenges
  • 1.2.1.1.2 Challenges of service quality
  • 1.2.1.1.3 Cost challenges
  • 1.2.1.1.4 Challenge of competitiveness
  • 1.2.1.2 Key objectives of Shift2Rail
  • 1.2.1.3 Main contents of Shift2Rail
  • 1.2.2 Development of smart railways in China
  • 1.2.2.1 Concept of smart railway
  • 1.2.2.2 Technical architecture and system of smart railway
  • 1.2.2.2.1 Technical architecture
  • 1.2.2.2.2 Technical system
  • 1.2.2.3 Construction of smart railway
  • 1.2.2.3.1 Test on Beijing-Shenyang smart high-speed railway
  • 1.2.2.3.2 Construction of smart Beijing-Zhangjiakou high-speed railway
  • 1.3 Smart traction power supply system
  • 1.3.1 Definition
  • 1.3.2 Structure and characteristics
  • 1.3.2.1 System architecture
  • 1.3.2.1.1 System architecture
  • 1.3.2.1.2 Level division
  • 1.3.2.2 System functions
  • 1.3.2.3 Technical characteristics
  • 1.3.2.3.1 Extrinsic characteristics
  • 1.3.2.3.2 Intrinsic characteristics
  • Summary
  • References
  • 2 Smart traction substations
  • 2.1 Overall architecture
  • 2.2 Smart primary equipment
  • 2.2.1 Overview
  • 2.2.2 Smart traction transformer
  • 2.2.2.1 Online monitoring items
  • 2.2.2.2 Online monitoring methods
  • 2.2.2.2.1 Temperature of winding and core
  • 2.2.2.2.2 Gases dissolved in oil
  • 2.2.2.2.3 Core grounding current
  • 2.2.2.3 Smart components for online monitoring
  • 2.2.2.3.1 Smart components and layout of sensors
  • 2.2.2.3.2 Smart component cabinet
  • 2.2.3 220kV smart circuit breaker.
  • 2.2.3.1 Online monitoring items
  • 2.2.3.2 Smart components and monitoring sensors
  • 2.2.3.2.1 Smart components
  • 2.2.3.2.2 Monitoring sensors
  • 2.2.4 27.5kV or 2×27.5kV smart GIS switchgear
  • 2.2.4.1 Online monitoring items
  • 2.2.4.2 Smart components and monitoring sensors
  • 2.2.4.2.1 Smart components
  • 2.2.4.2.2 Monitoring sensors
  • 2.2.5 220kV smart disconnector
  • 2.2.5.1 Online monitoring items
  • 2.2.5.2 Smart components and monitoring sensors
  • 2.2.5.2.1 Smart components
  • 2.2.5.2.2 Monitoring sensors
  • 2.2.5.2.3 Layout of monitoring unit for disconnector
  • 2.3 Smart secondary equipment
  • 2.3.1 Traction network protection and fault location
  • 2.3.1.1 Three-level feeder protection: local area, substation area, and wide area
  • 2.3.1.1.1 Local protection
  • 2.3.1.1.2 Substation area protection
  • 2.3.1.1.3 Wide-area protection
  • 2.3.1.2 Fault location and type judgment in traction network
  • 2.3.1.2.1 Definitions
  • 2.3.1.2.2 Location of fault in traction network
  • Fault location principle based on the AT neutral point boosting current ratio
  • Fault location principle based on the up and down current ratio
  • Fault l006Fcation principle based on the cross-connection line current ratio
  • 2.3.1.2.3 Enabling elements for fault location
  • Enabling by activation signal of feeder protection at traction substation
  • Enabling by low-voltage of traction network
  • 2.3.1.2.4 Fault type judgment of traction network
  • 2.3.2 Wide-area protection of traction network
  • 2.3.2.1 Fault analysis of all-parallel autotransformer network
  • 2.3.2.2 Wide-area protection for traction network based on current characteristics
  • 2.3.2.3 Wide-area protection for traction network based on impedance characteristics
  • 2.3.2.3.1 Setting values of protections 1 and 2 at traction substation
  • 2.3.2.3.2 Setting values of protections 3 and 4 at AT post.
  • 2.3.2.3.3 Setting values of protections 5 and 6 at section post
  • 2.3.3 Self-healing reconfiguration of traction power supply system
  • 2.3.3.1 Self-healing reconfiguration of traction substation
  • 2.3.3.1.1 Automatic switching to standby incoming line
  • Automatic switching to the 2# incoming line upon voltage loss in the 1# incoming line to enable shifting from opera...
  • Automatic switching to the 2# incoming line upon voltage loss in the 1# incoming line to enable shifting from opera...
  • 2.3.3.1.2 Automatic switching to standby traction transformer
  • Automatic switching to T2 when a fault occurs in the main transformer T1 to enable shifting from operation mode 1 to operat...
  • Automatic switching to T1 when a fault occurs in the main transformer T2 to enable shifting from operation mode 2 to operat...
  • 2.3.3.2 Self-healing reconfiguration of autotransformer AT post
  • 2.3.3.2.1 Automatic switching between ATs in the connection mode through circuit breakers
  • 2.3.3.2.2 Automatic switching between autotransformers in the connection mode through disconnectors
  • 2.3.3.3 Post-fault self-healing reconfiguration of traction network
  • 2.3.3.3.1 Case 1
  • Self-healing reconfiguration of traction network after feeder circuit breaker at traction substation bec...
  • 2.3.3.3.2 Case 2
  • Self-healing reconfiguration of traction network with uninterrupted power supply to up and down feeding ...
  • 2.3.3.3.3 Case 3
  • Self-healing reconfiguration of traction network with partial power losses of up and down feeding sections
  • 2.3.4 Application mode of smart traction substation
  • 2.4 Summary
  • References
  • 3 Smart OCS
  • 3.1 Introduction
  • 3.2 Overview
  • 3.3 Design philosophy of comprehensive detection and monitoring
  • 3.3.1 Characteristic Parameters of OCS
  • 3.3.1.1 Geometric parameters
  • 3.3.1.1.1 Static geometric parameters.
  • 3.3.1.1.2 Dynamic geometric parameters
  • 3.3.1.2 Electrical parameters
  • 3.3.1.3 Mechanical parameters
  • 3.3.1.4 Limits of characteristic parameters
  • 3.3.2 Technical status of parts and components and equipment in OCS
  • 3.3.3 Top-level design of comprehensive detection and monitoring
  • 3.4 OCS detection and monitoring method
  • 3.4.1 Detection method for characteristic parameters of OCS
  • 3.4.1.1 Detection method for geometric parameters
  • 3.4.1.1.1 Stereo-vision measurement method
  • Model 1: Pinhole camera model
  • Model 2: Line-scan camera model
  • Model 3: Measurement model of binocular line-scan camera
  • 3.4.1.1.2 Detection methods for geometric parameters based on stereo vision
  • 3.4.1.2 Electrical parameters-Detection method for pantograph-catenary arcing
  • 3.4.1.3 Mechanical parameters-Detection method for pantograph-catenary contact forces
  • 3.4.2 Inspection method for technical status of parts and components of OCS
  • 3.4.2.1 Object detection based on deep convolutional neural network
  • 3.4.2.1.1 Architecture of deep convolutional neural network
  • Fully connected layer
  • Convolutional layer
  • Activation function
  • Pooling layer
  • 3.4.2.1.2 Deep convolutional neural network training
  • Loss function
  • Backward propagation
  • 3.4.2.1.3 OCS components localization based on deep convolutional neural network
  • Region proposal-based object detection
  • Regression-based object detection
  • 3.4.2.1.4 Object detection based on cascade network
  • 3.4.2.1.5 Experimental Verification
  • Key components localization
  • Cascade localization of split pins
  • 3.4.2.2 Defect detection of OCS components based on deep ensemble learning
  • 3.4.2.2.1 Image feature extraction
  • 3.4.2.2.2 Transfer learning
  • Basic methods
  • Transfer learning based on deep convolutional neural networks
  • 3.4.2.2.3 Ensemble learning.
  • The role of ensemble learning
  • Error analysis of ensemble learning
  • Ensemble learning with diversity
  • 3.4.2.2.4 Empirical risk and structural risk
  • Structural Risk Minimization principle
  • Support vector machine
  • 3.4.2.2.5 Pin-missing detection based on deep ensemble classification
  • Architecture of the deep ensemble classifier
  • Training and prediction of the deep ensemble classifier
  • 3.4.2.2.6 Experimental verification
  • 3.4.2.3 Defect detection of OCS components based on deep unsupervised learning
  • 3.4.2.3.1 Autoencoder
  • Shallow autoencoder
  • Deep autoencoder
  • 3.4.2.3.2 Multi-task Learning
  • 3.4.2.3.3 Insulator defect detection based on deep denoising autoencoder
  • Insulator defect detection framework
  • Deep multi-task convolutional neural networks
  • Criteria for defect detection
  • 3.4.2.3.4 Experimental verification
  • 3.4.2.4 Defect detection of OCS components based on deep bayesian segmentation network
  • 3.4.2.4.1 Bayesian neural network
  • 3.4.2.4.2 Deep bayesian neural network
  • Training of deep neural networks
  • Stochastic regularization of deep neural networks
  • 3.4.2.4.3 Defect detection of OCS contact wire support based on deep bayesian segmentation network
  • Defect detection framework for OCS contact wire supports
  • Segmentation of contact wire supports components
  • Defect detection of CWS components
  • 3.4.2.4.4 Experimental verification
  • 3.4.2.5 Swivel clevis defect detection based on deep adaptive learning
  • 3.4.2.5.1 Adaptive learning
  • Reliability evaluation of the model
  • Update of the model
  • 3.4.2.5.2 Swivel clevis defect detection based on deep adaptive learning
  • Adaptive defect detection framework for swivel clevises
  • Adaptive Swivel Clevis segmentation
  • Swivel clevis defect detection
  • 3.4.2.5.3 Experimental verification
  • Swivel clevis segmentation.