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Fault Types01:18

Fault Types

73
When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...
73
Multimachine Stability01:25

Multimachine Stability

140
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
140
Block Diagram Reduction01:22

Block Diagram Reduction

155
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
155
Network Function of a Circuit01:25

Network Function of a Circuit

255
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
255
State Space to Transfer Function01:21

State Space to Transfer Function

171
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
171
Small-signal Diode Model01:18

Small-signal Diode Model

741
In analyzing the behavior of diodes in circuits, the relationship between the current through a diode and the voltage across it is of particular interest, especially when considering the effect of a direct current (DC) bias voltage. When applied, this DC bias influences the diode's operating point, known as the Q point, around which the current-voltage (I-V) characteristic of the diode exhibits exponential behavior. Introducing a small, time-varying signal on top of this bias aids in...
741

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A Robust Transmission Scheduling Approach for Internet of Things Sensing Service with Energy Harvesting.

Sensors (Basel, Switzerland)ยท2019
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Related Experiment Video

Updated: Jun 3, 2025

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A Game Model and Fault Recovery Algorithm for SDN Multi-Domain.

Tao Xu1,2, Chen Chen1, Kaiming Hu1

  • 1The College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.

Sensors (Basel, Switzerland)
|January 11, 2025
PubMed
Summary

This study introduces a game-enhanced algorithm for fast fault recovery in Software-Defined Networking (SDN) controllers for Wireless Sensor Networks (WSNs). The method minimizes migration costs and controller load, significantly reducing recovery time.

Keywords:
fault recoverygame domainsoftware-defined network (SDN)

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Area of Science:

  • Computer Science
  • Network Engineering
  • Distributed Systems

Background:

  • Software-Defined Networking (SDN) enables flexible Wireless Sensor Network (WSN) management by decoupling control logic.
  • SDN controller failures pose a significant challenge to network stability and performance.
  • Existing fault recovery mechanisms often struggle with efficiency and scalability in multi-domain environments.

Purpose of the Study:

  • To propose a novel game-theoretic approach for autonomous fault recovery in SDN controllers.
  • To develop an algorithm that ensures timely and cost-effective recovery from controller failures.
  • To enhance the resilience and reliability of WSNs managed by SDN.

Main Methods:

  • A game-theoretic model for multi-domain SDN controller fault recovery is developed.
  • A game-enhanced autonomous fault recovery algorithm is proposed, incorporating controller capacity and device transition relationships.
  • Linear programming and a multi-population particle swarm optimization algorithm are used for optimal switch mapping and migration.
  • The algorithm iteratively optimizes controller-switch mapping to minimize migration costs and load.

Main Results:

  • The proposed algorithm achieves fast fault recovery with low migration costs for SDN controllers.
  • It effectively balances switch migration costs and controller load pressure.
  • Experimental results show a significant reduction in fault recovery time compared to existing methods.
  • Reduced propagation delay in SDN controllers is observed.

Conclusions:

  • The game-enhanced autonomous fault recovery algorithm offers a robust solution for SDN controller failures in WSNs.
  • The approach enhances network resilience by optimizing recovery processes.
  • This method provides a significant improvement in fault recovery speed and resource management efficiency.