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dc.contributor.advisorValenzuela, Jorge
dc.contributor.authorAldubaisi, Ali
dc.date.accessioned2020-05-15T17:44:18Z
dc.date.available2020-05-15T17:44:18Z
dc.date.issued2020-05-15
dc.identifier.urihttp://hdl.handle.net/10415/7219
dc.description.abstractThe wind industry is facing new challenges due to the rapid growth of wind farms all around the world. Farm size, turbine size and a rising number of sophisticated components, the industry has to face the challenge to develop sustainable maintenance procedures in order to meet the planned cost and time. Important limiting factors for wind farms life cycle management are weather conditions and maintenance strategies. Hence, optimal strategies need to take weather conditions into account. In this context, the focus of this research is to develop an effective approach to maintain wind farms while incorporating the effects of weather conditions on aging and accessibility. This is achieved through the implementation of a discrete-event simulation approach which captures the impact of environmental conditions (wind speed, temperature) on the aging process of wind turbines. First, we explore SCADA data of four wind turbines to study the impact of wind speed and air temperature on the uptime of wind turbines. This built-in system collects operational and ambient data at 10-minute resolution. Statistical analysis on SCADA data has shown significant impact of high wind speed on uptime. We investigate two years SCADA data of four turbines based on status and fault codes to evaluate the impact of weather condition during each uptime periods. The four wind turbines are identical Senvion MM82. This type of turbines is a variable-speed, three bladed with a hub height of 78 m and a rotor diameter of 82 m. They operate within a wind speed range between 3 m/s and 25 m/s. Results have shown that uptime is significantly reduced if the average wind speed during that period is higher than 7 m/s. On the other hand, the effect of air temperature on uptime is not clear and a larger dataset is needed to investigate further. Second, we propose a new weather-based maintenance approach for wind turbine systems based on Equivalent Age models, which account for the effect of wind speed and ambient temperature on turbine aging. We develop a weather based age prediction model and the associated ii maintenance threshold values for devising optimal maintenance strategies that reduce the maintenance costs while enhancing the availability of wind power. We consider a single wind turbine subject to variable weather conditions. We examine wind speed and air temperature and how they affect the aging of the turbine and the feasibility of maintenance. This makes the proposed age modeling and resulting maintenance strategy weather-dependent. Finally, we propose a farm-level equivalent age-based opportunistic maintenance threshold driven by wind speed data. Maintenance decisions consider the trade-off between the optimal preventive maintenance schedule for individual turbines and the cost reduction due to the grouping maintenance actions of multiple turbines together in one maintenance event. Economic and accessibility constraints are also considered. We use a simulation approach to experiment with different weather conditions, economic dependencies, and location scenarios . We then present a numerical example to find the optimal maintenance thresholds for a wind farm under different weather conditions. Experiments conducted on a 100-turbine wind farm case demonstrate the advantages of our proposal over traditional policies. The model is solved using the Nelder–Mead method which produces scenario specific results. We highlight the benefits of our approach through a numerical example using hourly wind speed and air temperature data from three different regions in the United States. The results show an optimal policy can be adapted to weather conditions, showing the cost-effective action for each specific location. Compared with traditional age-based maintenance, our approach can achieve improvement in both availability and costs.en_US
dc.rightsEMBARGO_NOT_AUBURNen_US
dc.subjectIndustrial and Systems Engineeringen_US
dc.titleEquivalent Age-based Opportunistic Maintenance for Wind Farmsen_US
dc.typePhD Dissertationen_US
dc.embargo.lengthMONTHS_WITHHELD:24en_US
dc.embargo.statusEMBARGOEDen_US
dc.embargo.enddate2022-05-02en_US


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