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<p>This paper introduces a comprehensive dynamic reliability assessment framework for doubly-fed induction generator (DFIG)-based wind power systems, considering wind-speed-dependent component hazard rates. A bottom-up physical failure model is constructed by investigating the individual, operationally stressed failure trajectories of the key subsystems, including the blade assembly, generator, power electronics converters, transformer, and transmission cables. The results show that the equivalent failure rate is significantly modulated by the power loading profile of the turbine, with increased failure risk in the 10–15&#xa0;m/s wind speed range due to maximum electro-thermal and mechanical stresses. Hourly availability and adequacy indices are quantified using a sequential Monte Carlo simulation over a one-year operational horizon. The proposed dynamic framework is rigorously evaluated against a traditional constant-failure-rate (CFR) baseline. The traditional method yields an optimistic LOLE of 3410.28&#xa0;h/year, whereas the proposed wind-speed-dependent model indicates a non-conservative estimation error of 45.27%, with a more realistic risk profile of 4954.00&#xa0;h/year and a LOLP of 56.55%. The results highlight the relevance of incorporating dynamic reliability models in stochastic planning frameworks and provide a powerful decision-making tool for the asset management and grid hosting configuration of large-scale renewable generation systems.</p>

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Multi-layer reliability assessment of DFIG-based wind turbines under variable wind conditions

  • 期刊论文

  • 发表日期：

    2026年7月3日

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  • Amir Ghaedi,
  • Mahmood Allahyari,
  • Ghodrat Karami,
  • Alireza Mirzaee

摘要

This paper introduces a comprehensive dynamic reliability assessment framework for doubly-fed induction generator (DFIG)-based wind power systems, considering wind-speed-dependent component hazard rates. A bottom-up physical failure model is constructed by investigating the individual, operationally stressed failure trajectories of the key subsystems, including the blade assembly, generator, power electronics converters, transformer, and transmission cables. The results show that the equivalent failure rate is significantly modulated by the power loading profile of the turbine, with increased failure risk in the 10–15 m/s wind speed range due to maximum electro-thermal and mechanical stresses. Hourly availability and adequacy indices are quantified using a sequential Monte Carlo simulation over a one-year operational horizon. The proposed dynamic framework is rigorously evaluated against a traditional constant-failure-rate (CFR) baseline. The traditional method yields an optimistic LOLE of 3410.28 h/year, whereas the proposed wind-speed-dependent model indicates a non-conservative estimation error of 45.27%, with a more realistic risk profile of 4954.00 h/year and a LOLP of 56.55%. The results highlight the relevance of incorporating dynamic reliability models in stochastic planning frameworks and provide a powerful decision-making tool for the asset management and grid hosting configuration of large-scale renewable generation systems.