• Energy Research

By utilizing condition monitoring information collected from wind turbine components, condition based maintenance (CBM) strategy can be used to reduce the operation and maintenance costs of wind power generation systems. The existing CBM methods for wind power generation systems deal with wind turbine components separately, that is, maintenance decisions are made on individual components, rather than the whole system. However, a wind farm generally consists of multiple wind turbines, and each wind turbine has multiple components including main bearing, gearbox, generator, etc. There are economic dependencies among wind turbines and their components. That is, once a maintenance team is sent to the wind farm, it may be more economical to take the opportunity to maintain multiple turbines, and when a turbine is stopped for maintenance, it may be more cost-effective to simultaneously replace multiple components which show relatively high risks. In this paper, we develop an optimal CBM solution to the above-mentioned issues. The proposed maintenance policy is defined by two failure probability threshold values at the wind turbine level. Based on the condition monitoring and prognostics information, the failure probability values at the component and the turbine levels can be calculated, and the optimal CBM decisions can be made accordingly. A simulation method is developed to evaluate the cost of the CBM policy. A numerical example is provided to illustrate the proposed CBM approach. A comparative study based on commonly used constant-interval maintenance policy demonstrates the advantage of the proposed CBM approach in reducing the maintenance cost.

By utilizing condition monitoring information collected from wind turbine components, condition based maintenance (CBM) strategy can be used to reduce the operation and maintenance costs of wind power generation systems. The existing CBM methods for wind power generation systems deal with wind turbine components separately, that is, maintenance decisions are made on individual components, rather than the whole system. However, a wind farm generally consists of multiple wind turbines, and each wind turbine has multiple components including main bearing, gearbox, generator, etc. There are economic dependencies among wind turbines and their components. That is, once a maintenance team is sent to the wind farm, it may be more economical to take the opportunity to maintain multiple turbines, and when a turbine is stopped for maintenance, it may be more cost-effective to simultaneously replace multiple components which show relatively high risks. In this paper, we develop an optimal CBM solution to the above-mentioned issues. The proposed maintenance policy is defined by two failure probability threshold values at the wind turbine level. Based on the condition monitoring and prognostics information, the failure probability values at the component and the turbine levels can be calculated, and the optimal CBM decisions can be made accordingly. A simulation method is developed to evaluate the cost of the CBM policy. A numerical example is provided to illustrate the proposed CBM approach. A comparative study based on commonly used constant-interval maintenance policy demonstrates the advantage of the proposed CBM approach in reducing the maintenance cost.

Abstract Few methods are available for optimizing corrective maintenance and time-based maintenance for wind farms, although these strategies are currently widely used in practice. Economic dependencies exist among wind turbine systems and their components in the wind farm. That is, it may be more economical to maintain multiple turbines or turbine components when a corrective or preventive maintenance opportunity presents. In this paper, opportunistic maintenance approaches are developed for wind farms to take advantage of the maintenance opportunities. Imperfect maintenance actions are considered, which addresses the practical issue that preventive maintenance does not always return components to as-good-as-new status. The proposed opportunistic maintenance policies are defined by the component's age threshold values, and different imperfect maintenance thresholds are introduced for failure turbines and working turbines. Three types of preventive maintenance actions are considered, including perfect, imperfect and two-level action. Simulation methods are developed to evaluate the costs of proposed opportunistic maintenance policies. Numerical examples are provided to illustrate the proposed approaches. Comparative study with the widely used corrective maintenance policy demonstrates the advantage of the proposed opportunistic maintenance methods in significantly reducing the maintenance cost. The developed methods are expected to bring immediate benefits to wind power industry.

Abstract Few methods are available for optimizing corrective maintenance and time-based maintenance for wind farms, although these strategies are currently widely used in practice. Economic dependencies exist among wind turbine systems and their components in the wind farm. That is, it may be more economical to maintain multiple turbines or turbine components when a corrective or preventive maintenance opportunity presents. In this paper, opportunistic maintenance approaches are developed for wind farms to take advantage of the maintenance opportunities. Imperfect maintenance actions are considered, which addresses the practical issue that preventive maintenance does not always return components to as-good-as-new status. The proposed opportunistic maintenance policies are defined by the component's age threshold values, and different imperfect maintenance thresholds are introduced for failure turbines and working turbines. Three types of preventive maintenance actions are considered, including perfect, imperfect and two-level action. Simulation methods are developed to evaluate the costs of proposed opportunistic maintenance policies. Numerical examples are provided to illustrate the proposed approaches. Comparative study with the widely used corrective maintenance policy demonstrates the advantage of the proposed opportunistic maintenance methods in significantly reducing the maintenance cost. The developed methods are expected to bring immediate benefits to wind power industry.

Abstract Wind power is a significant clean energy source. Operation & maintenance (O&M) costs account for about 25% of the cost of wind power, and it is critical to improve the reliability of wind power generators to reduce the overall cost and increase wind power competitiveness comparing to other power sources. Wind turbines are subject to instantaneously varying load due to wind turbulence, which challenges the prognostic study for predicting equipment future health conditions and remaining useful lives. With existing prognostics methods, the average constant load is typically used to approximate the varying external load. In this paper, an integrated varying-load approach is proposed for predicting wind turbine gearbox remaining useful life by specifically considering instantaneously varying external load, which is more realistic. Fatigue crack damage is focused on. The method integrates gear physical models and available health condition data, and the distribution of uncertain material parameter modeled in crack degradation process is updated via Bayesian inference once new health condition data become available. Examples are provided to demonstrate the effectiveness of the proposed varying-load approach. A comparative study is conducted between the proposed approach and existing constant-load approximation method, and the results show that the proposed varying-load approach can provide more accurate prediction.

Abstract Wind power is a significant clean energy source. Operation & maintenance (O&M) costs account for about 25% of the cost of wind power, and it is critical to improve the reliability of wind power generators to reduce the overall cost and increase wind power competitiveness comparing to other power sources. Wind turbines are subject to instantaneously varying load due to wind turbulence, which challenges the prognostic study for predicting equipment future health conditions and remaining useful lives. With existing prognostics methods, the average constant load is typically used to approximate the varying external load. In this paper, an integrated varying-load approach is proposed for predicting wind turbine gearbox remaining useful life by specifically considering instantaneously varying external load, which is more realistic. Fatigue crack damage is focused on. The method integrates gear physical models and available health condition data, and the distribution of uncertain material parameter modeled in crack degradation process is updated via Bayesian inference once new health condition data become available. Examples are provided to demonstrate the effectiveness of the proposed varying-load approach. A comparative study is conducted between the proposed approach and existing constant-load approximation method, and the results show that the proposed varying-load approach can provide more accurate prediction.