Publication record · 18.cifr/2020.peyghami.pe-reliability-maintenance

System-Level Design for Reliability and Maintenance Scheduling in Modern Power Electronic-Based Power Systems

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Saeed Peyghami (Aalborg University), Peter Palensky (Delft University of Technology), Mahmoud Fotuhi-Firuzabad (Sharif University of Technology), Frede Blaabjerg (Aalborg University)

RAI18.cifr/2020.peyghami.pe-reliability-maintenance

IEEE Open Access Journal of Power and Energy· 2020· doi:10.1109/OAJPE.2020.3029229

This paper presents a system-level design framework for reliability and maintenance scheduling in modern power electronic-based power systems. Mission-profile-based stress analysis is combined with component lifetime models to obtain failure rates. Markov chain models compute system reliability indices. Maintenance scheduling optimization minimizes lifecycle cost subject to availability constraints.

power electronicsreliabilitymaintenance schedulingMarkov chainmission profileavailabilityMTTF

✦ Research context

What this agent contributes to the literature.

Problem solved

Power system designers lack a unified framework linking component stress, failure rates, and maintenance scheduling.
Traditional reliability methods do not account for mission-profile-driven stress variation in power electronic converters.
Maintenance intervals are often set empirically, leading to excessive cost or unmet availability targets.

Novelty

Combines mission-profile-based stress analysis with Markov-chain models for system-level reliability in power-electronic systems.

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Canvas contract1-in / 1-out · unpacked into system, maintenance legacy ports

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sha256:bfe6c2aa1b00e564a4ff8a7e6aaddcc574119e8205045a5c12212c8939d9c23f

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input:application/json

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Pre-filled with the paper's canonical scenario. Click Invoke agent to reproduce the original result, or edit the JSON below to run a counterfactual.

inputapplication/jsonoptional

Unified canvas input. The executor unpacks this JSON blob into legacy per-port files: system, maintenance

Leave empty to run the paper's canonical scenario.

{
  "system": {
    "description": "5-component DC-AC power electronic converter system (3-level NPC + filter + controller)",
    "time_horizon_hours": 87600,
    "mission_profile": {
      "junction_temperature_C": 70,
      "load_ratio": 0.85
    },
    "components": [
      {
        "id": "IGBT_S1",
        "type": "IGBT",
        "description": "Upper switch IGBT module",
        "base_failure_rate": 0.0000012,
        "repair_rate": 0.001,
        "junction_temperature_C": 75,
        "load_ratio": 0.85
      },
      {
        "id": "IGBT_S2",
        "type": "IGBT",
        "description": "Lower switch IGBT module",
        "base_failure_rate": 0.0000012,
        "repair_rate": 0.001,
        "junction_temperature_C": 72,
        "load_ratio": 0.85
      },
      {
        "id": "CAP_DC",
        "type": "Capacitor",
        "description": "DC-link electrolytic capacitor",
        "base_failure_rate": 0.0000025,
        "repair_rate": 0.0008,
        "junction_temperature_C": 65,
        "load_ratio": 0.8
      },
      {
        "id": "INDUCTOR_L",
        "type": "Inductor",
        "description": "Output filter inductor",
        "base_failure_rate": 5e-7,
        "repair_rate": 0.0012,
        "junction_temperature_C": 55,
        "load_ratio": 0.9
      },
      {
        "id": "GATE_DRV",
        "type": "GateDriver",
        "description": "Gate driver circuit",
        "base_failure_rate": 8e-7,
        "repair_rate": 0.0015,
        "junction_temperature_C": 50,
        "load_ratio": 0.7
      }
    ],
    "topology": {
      "type": "series",
      "components": [
        {
          "type": "parallel",
          "components": [
            "IGBT_S1",
            "IGBT_S2"
          ]
        },
        "CAP_DC",
        "INDUCTOR_L",
        "GATE_DRV"
      ]
    }
  },
  "maintenance": {
    "description": "Maintenance parameters for the power electronic converter system",
    "target_availability": 0.999,
    "pm_effectiveness": 0.75,
    "pm_duration_hours": 8,
    "interval_search_min_hours": 500,
    "interval_search_max_hours": 15000,
    "corrective_cost_per_failure": 15000,
    "preventive_cost_per_event": 1500,
    "time_horizon_hours": 87600,
    "crew_availability": 0.95
  }
}

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