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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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A Reliability Assessment Method for Complex Systems Based on Non-Homogeneous Markov Processes.

Xiaolei Pan1,2, Hongxiao Chen1,2, Ao Shen1,2

  • 1College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China.

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|June 19, 2024
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Summary
This summary is machine-generated.

A new method using non-homogeneous Markov processes overcomes the curse of dimensionality for complex system reliability assessment. It decomposes systems into subsystems, enabling accurate analysis of intricate structures like the Reactor Protection System (RPS).

Keywords:
complex systemscurse of dimensionalitynon-homogeneous Markov processesreliability assessment

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Area of Science:

  • Reliability Engineering
  • System Dynamics
  • Stochastic Processes

Background:

  • The Markov method is standard for assessing system reliability, capturing dynamic behaviors like repairability and degradation.
  • The "curse of dimensionality" challenges Markov-based reliability assessments for complex systems due to state space explosion.
  • Existing methods struggle with the exponential growth of state spaces in complex system reliability analysis.

Purpose of the Study:

  • To propose a novel reliability assessment method for complex systems using non-homogeneous Markov processes.
  • To address the limitations of traditional Markov methods when dealing with high-dimensional state spaces.
  • To provide an accurate and effective approach for evaluating the reliability of complex systems.

Main Methods:

  • Decomposition of complex systems into multilevel subsystems with manageable state spaces based on system function.
  • Development of homogeneous or non-homogeneous Markov models for each subsystem and the overall system.
  • An algorithm to convert subsystem unavailability curves into 2x2 dynamic state transition probability matrices (STPMs) for upper-level model input.

Main Results:

  • The proposed method successfully models complex systems by integrating subsystem reliability data into higher-level analyses.
  • A case study on the Reactor Protection System (RPS) demonstrated the method's effectiveness and accuracy.
  • Comparison with existing methods confirmed the superiority of the novel approach in reliability assessment.

Conclusions:

  • The proposed non-homogeneous Markov process-based method effectively overcomes the curse of dimensionality in complex system reliability.
  • The subsystem decomposition and STPM conversion algorithm provide a robust framework for analyzing intricate systems.
  • The method offers a validated and accurate solution for reliability assessment in complex engineering applications.