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Related Concept Videos

Bus Impedance Matrix01:24

Bus Impedance Matrix

Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
In the first circuit, all machine voltage sources are short-circuited, leaving only the prefault voltage source at the fault location. The positive-sequence bus impedance matrix can be determined by solving the nodal equations,...
Multimachine Stability01:25

Multimachine Stability

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:
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...

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Related Experiment Videos

Trust index based fault tolerant multiple event localization algorithm for WSNs.

Xianghua Xu1, Xueyong Gao, Jian Wan

  • 1Grid and Services Computing Lab, School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310037, China. xhxu@hdu.edu.cn

Sensors (Basel, Switzerland)
|December 14, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a trust index model and the TISNAP algorithm to improve wireless sensor network event localization accuracy. It effectively mitigates faulty node impacts for better fault tolerance and reliable event detection.

Keywords:
binary datafault tolerancemaximum likelihood estimationmultiple event localizationtrust indexwireless sensor networks

Related Experiment Videos

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless sensor networks (WSNs) are crucial for event detection and localization.
  • Node faults can significantly degrade the accuracy of multiple event localization in WSNs.
  • Existing methods struggle to maintain accuracy when node fault rates are high.

Purpose of the Study:

  • To develop a robust algorithm for multiple event source localization in WSNs.
  • To enhance fault tolerance and accuracy by addressing the impact of faulty sensor nodes.
  • To introduce a trust index model for evaluating information fidelity from sensor nodes.

Main Methods:

  • Proposed the Trust Index based Subtract on Negative Add on Positive (TISNAP) localization algorithm.
  • Implemented a trust index model to assess the reliability of sensor node data.
  • Developed a three-phase algorithm involving cluster identification, likelihood matrix construction, and trust index updating.

Main Results:

  • The TISNAP algorithm significantly improves localization accuracy and fault tolerance.
  • Accurate event number determination and localization were achieved even with up to 50% node fault probability.
  • Demonstrated superior performance compared to other localization algorithms in experimental results.

Conclusions:

  • The TISNAP algorithm effectively reduces the impact of faulty nodes on multiple event localization.
  • The proposed trust index model enhances the reliability of WSNs in event detection scenarios.
  • This approach offers a promising solution for accurate and fault-tolerant event localization in challenging WSN environments.