A Preventive Maintenance Planning Approach for Wind Converters

Davoodi, Amirali; Peyghami, Saeed; Yang, Yongheng; Dragičević, Tomislav; Blaabjerg, Frede

doi:10.1109/egrid48559.2020.9330628

Cited by 5 publications

(5 citation statements)

References 21 publications

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“…For example, if N c +N r = 3 and β i = 1, then δ sm i,1 = 1 and δ sm i,2 = δ sm i,3 = 0, as the failure probability of submodule 1 is the highest. Then, regarding the example of (4), after ranking the failure probabilities of submodules 1 to 3 from the highest to the lowest, using the homogeneity of submodules, (4) can be equivalently written in T-form as shown in (5). In ( 5), the binary variables z i,0 = 1 to z i,3 = 1 represent replacing submodules 0 to 3 by new ones, respectively.…”

Section: The Failure Probability Model Of An Arm In the Proposed T-fo...mentioning

confidence: 99%

“…For example, in [4], a method is proposed to estimate the preventive maintenance period for MMCs. In [5], a periodical preventive maintenance strategy is proposed regarding random-chance failure probabilities, wear-out failure distributions, etc. Although periodic preventive maintenance can efficiently avoid the occurrence of failures, over-maintenance and also lack of maintenance may occur because of improper maintenance periods [6], [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Tractable Failure Probability Prediction Model for Predictive Maintenance Scheduling of Large-Scale Modular-Multilevel-Converters

Peyghami

Núñez

et al. 2023

IEEE Trans. Power Electron.

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Modular-multilevel-converters (MMCs) are vital components in direct current transmission networks. Predictive maintenance scheduling of MMCs requires estimations of the failure probabilities of MMCs during a period of time in the future. Particularly, the predicted future failure probabilities are influenced by two main factors, the mission profiles of the MMCs and the maintenance decisions on the MMCs during the prediction period. This paper proposes a failure probability prediction model (FPM) for MMCs by considering these two factors. First, the expectations of the failure probabilities of the components for all the scenarios of mission profiles are obtained. Second, in predictive maintenance scheduling problems, the decisions to perform the maintenance actions are represented by binary variables. When the number of submodules is very large, using the binomial probability form currently used in reliability engineering to express the "r-out-of-n" failure probability of arms of the MMCs is intractable. Thus, this paper proposes a tractable form (T-form) in FPM by observing that the submodules on one arm are homogeneous. Furthermore, an approximation method, i.e., clustering and assignment (C&A), is proposed to reduce the computation times for calculating the parameters needed by the proposed T-form. Then, we perform a case study that assesses the accuracy and computation time of the C&A approach. The results show that the accuracy of the C&A approach is high and that the computation time is reduced significantly compared with the accurate method. We also show that the computation time for solving the predictive maintenance scheduling problem can be reduced hugely by using the T-form instead of the binomial probability form.

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Section: The Failure Probability Model Of An Arm In the Proposed T-fo...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Tractable Failure Probability Prediction Model for Predictive Maintenance Scheduling of Large-Scale Modular-Multilevel-Converters

Peyghami

Núñez

et al. 2023

IEEE Trans. Power Electron.

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“…Zheng et al [2] looks into the effects of varying wind speeds on wind turbine maintenance planning. Davoodi et al [3] singles out the converter as a crucial component of the wind turbine and builds an optimization model to find the optimal replacement times for the converters. Wang et al [4] and Zhang et al [5] deal with imperfect preventive maintenance.…”

Section: Introductionmentioning

confidence: 99%

Optimal Preventive Maintenance Scheduling for Wind Turbines under Condition Monitoring

Yu,

Bangalore,

Fogelström

et al. 2024

Energies

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Renewable energy sources, such as wind and solar, are positioned to play a pivotal role in future energy systems. In this paper, we propose a mathematical model for calculating and regularly updating the next preventive maintenance plan for a wind farm. Our optimization criterion considers various factors, including the current ages of key components, major maintenance costs, eventual energy production losses, and available data monitoring the condition of the wind turbines. Employing Cox proportional hazards analysis, we develop a comprehensive approach that accounts for the current ages of critical components, significant maintenance costs, potential energy production losses, and data collected from monitoring the condition of wind turbines. We illustrate the effectiveness of our approach through a case study based on data collected from multiple wind farms in Sweden. Our results demonstrate that preventive maintenance planning yields positive effects, particularly when the wind turbine components in question have significantly shorter lifespans than the turbine itself.

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“…Paper [2] looks into the effects of the varying wind speed on the wind turbine maintenance planning. Paper [3] singles out the converter as a crucial component of the wind turbine and builds an optimization model to find the optimal replacement times for the converters. Papers [4] and [5] deal with imperfect preventive maintenance.…”

Section: Introductionmentioning

confidence: 99%

Optimal preventive maintenance scheduling for wind turbines under condition monitoring

Yu,

Bangalore,

Fogelström

et al. 2021

Preprint

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We suggest a mathematical model for computing and regularly updating the next preventive maintenance plan for a wind farm. Our optimization criterium takes into account the current ages of the key components, the major maintenance costs including eventual energy production losses as well as the available data monitoring the condition of the wind turbines. We illustrate our approach with a case study based on data collected from several wind farms located in Sweden. Our results show that preventive maintenance planning gives some effect, if the wind turbine components in question live significantly shorter than the turbine itself.

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A Preventive Maintenance Planning Approach for Wind Converters

Cited by 5 publications

References 21 publications

A Tractable Failure Probability Prediction Model for Predictive Maintenance Scheduling of Large-Scale Modular-Multilevel-Converters

A Tractable Failure Probability Prediction Model for Predictive Maintenance Scheduling of Large-Scale Modular-Multilevel-Converters

Optimal Preventive Maintenance Scheduling for Wind Turbines under Condition Monitoring

Optimal preventive maintenance scheduling for wind turbines under condition monitoring

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