Case Study of Weather Maintenance in Wind Power Generation

Costa, Ángel M.; Roshan, Gholamreza; Orosa, José A.; Rodríguez-Fernández, Ángel

doi:10.1007/s13369-014-1115-6

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…This research work aims to determine the existence of a relation between the climatic variables that usually occur in a wind farm and the occurrence of the failure at the time each of them happens. This study, despite the fact it was initially analyzed in previous studies [ 6 , 11 , 25 ], is a more detailed analysis of the relationship between a nonfailure and a failure has been attempted according to each of the climatic variables present at the time of the failure, as shown in Table 5 . This table shows the errors that, compared to the nonfailure, show for each of the climate variables a significance level close to 0.000, that is, they are different from the condition’s climate failure.…”

Section: Discussionmentioning

confidence: 99%

“…Other investigations continued to reaffirm the relationship of weather conditions, failure rate and failure modes [ 9 , 10 ]. In the same way, an initial climatic variable-failure classification, by analysis of the variance, was developed to obtain control algorithms in subsequent research works to predict breakdowns and improve the maintenance systems by condition [ 11 ]. Finally, in 2017, a new study showed that failure prediction models based only on the age of wind turbine systems and components can be improved by introducing the climatic conditions to which they are exposed to.…”

Section: Introductionmentioning

confidence: 99%

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The Influence of Climate Parameters on Maintenance of Wind Farms—A Galician Case Study

Orosa

Costa

Vergara

et al. 2020

Sensors

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There are different monitoring procedures in wind farms with two main objectives: (i) to improve energy production by the capability of the national electrical network and (ii) to reduce the stooped hours due to preventive and or corrective maintenance activities. In this sense, different sensors are employed to sample in real-time the working conditions of equipment, the electrical production and the weather conditions. Despite this, just the anemometer measurement can be related to the more important errors of interruption of power regulation and anemometer errors. Both errors are related to gusty winds and contribute to more than 33% of the cost of a wind farm. The present paper reports some mathematical relations between weather and maintenance but there are no extreme values of each variable that let us predict a near failure and its corresponding loss of working hours. To achieve this, statistical analysis identifies the relation between weather variables and errors and different models are obtained. What is more, due to the difficulty and economic implications involving the implementation of complex algorithms and techniques of artificial intelligence, it is still a challenge to optimize this process. Finally, the obtained results show a particular case study that can be extrapolated to other wind farms after different case studies to adjust the model to different weather regions, and serve as a useful tool for weather maintenance.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Influence of Climate Parameters on Maintenance of Wind Farms—A Galician Case Study

Orosa

Costa

Vergara

et al. 2020

Sensors

Self Cite

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“…Costa at al. [10] stated that correlating relative humidity and failures using classical analysis approaches, such as the analysis of variance (ANOVA) technique, is one of the most challenging tasks. Tavner et al [11] cross-correlated component failures with average monthly maximum and mean wind speed, maximum and minimum air temperature and average daily mean relative humidity.…”

Section: Background In Wind Energymentioning

confidence: 99%

Data-driven learning framework for associating weather conditions and wind turbine failures

Reder

Yürüşen

Melero

2018

Reliability Engineering & System Safety

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The need for cost effective operation and maintenance (O&M) strategies in wind farms has risen significantly with the growing wind energy sector. In order to decrease costs, current practice in wind farm O&M is switching from corrective and preventive strategies to rather predictive ones. Anticipating wind turbine (WT) failures requires sophisticated models to understand the complex WT component degradation processes and to facilitate maintenance decision making. Environmental conditions and their impact on WT reliability play a significant role in these processes and need to be investigated profoundly. This paper is presenting a framework to assess and correlate weather conditions and their effects on WT component failures. Two approaches, using (a) supervised and (b) unsupervised data mining techniques are applied to pre-process the weather and failure data. An apriori rule mining algorithm is employed subsequently, in order to obtain logical interconnections between the failure occurrences and the environmental data, for both approaches. The framework is tested using a large historical failure database of modern wind turbines. The results show the relation between environmental parameters such as relative humidity, ambient temperature, wind speed and the failures of five major WT components: gearbox, generator, frequency converter, pitch and yaw system. Additionally, the performance of each technique, associating weather conditions and WT component failures, is assessed.

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“…Wilson and McMillan 14,15 used a Markov chain to describe the dependency between the hazard rate of wind turbines and wind speed. Costa et al 16 presented a procedure to obtain a control algorithm based on wind speed and temperature to forecast faults and improve maintenance. Restricting maintenance implementation. The maintenance implementation of wind turbines usually requires the availability of various resources such as facilities, technicians, and spare parts.…”

Section: Introductionmentioning

confidence: 99%

Optimal preventive maintenance for wind turbines considering the effects of wind speed

2020

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This paper presents a preventive maintenance policy for wind turbines considering three effects of wind speed: accelerating hazard rate, restricting maintenance implementation, and affecting downtime cost. The effect on the hazard rate is described by a proportional hazards model. The restriction on maintenance actions results in the elongation of maintenance duration. The downtime cost is evaluated based on the power output curve model. Three failure types (i.e., catastrophic failure, critical failure, and minor failure) and multiple maintenance actions (i.e., preventive and corrective replacements, preventive and corrective general repairs, and minimal repair) are integrated into the policy. The objective is to determine the optimal maintenance policy that minimizes the long-run average cost rate. The optimization problem is formulated and solved in the semi-Markov decision process (SMDP) framework. A practical example is provided to illustrate the proposed method. The comparison with other policies shows the superiority of the proposed policy as well as the significance of the three effects of wind speed in maintenance decision-making.

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Case Study of Weather Maintenance in Wind Power Generation

Cited by 8 publications

References 8 publications

The Influence of Climate Parameters on Maintenance of Wind Farms—A Galician Case Study

The Influence of Climate Parameters on Maintenance of Wind Farms—A Galician Case Study

Data-driven learning framework for associating weather conditions and wind turbine failures

Optimal preventive maintenance for wind turbines considering the effects of wind speed

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