arXiv subjects

Quanjiang Yu

Publications and source records attributed to Quanjiang Yu.

4 recordsLinked to original sources

arXiv2021

Mathematical optimization models for long-term maintenance scheduling of wind power systems

During the life of a wind farm, various types of costs arise. A large share of the operational cost for a wind farm is due to maintenance of the wind turbine equipment; these costs are especially pronounced for offshore wind farms and provide business opportunities in the wind energy industry. An effective scheduling of the maintenance activities may reduce the costs related to maintenance. We combine mathematical modelling of preventive maintenance scheduling with corrective maintenance strategies. We further consider different types of contracts between the wind farm owner and a maintenance or insurance company, and during different phases of the turbines' lives and the contract periods. Our combined preventive and corrective maintenance models are then applied to relevant combinations of the phases of the turbines' lives and the contract types. Our case studies show that even with the same initial criteria, the optimal maintenance schedules differ between different phases of time as well as between contract types. One case study reveals a 40% cost reduction and a significantly higher production availability -- 1.8% points -- achieved by our optimization model as compared to a pure corrective maintenance strategy. Another study shows that the number of planned preventive maintenance occasions for a wind farm decreases with an increasing level of an insurance contract regarding reimbursement of costs for broken components.

math.OC↗

arXiv2021

Optimal preventive maintenance scheduling for wind turbines under condition monitoring

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.

math.OC↗

arXiv2020

Optimal maintenance schedule for a wind turbine with aging components

Wind power is one of the most important sources of renewable energy. A large part of the wind energy cost is due to the cost of maintaining the wind power equipment. To further reduce the maintenance cost, one can improve the design of the wind turbine components. One can also reduce the maintenance costs by optimal scheduling of the component replacements. The latter task is the main motivation for this paper. When a wind turbine component fails to function, it might need to be replaced under less than ideal circumstances. This is known as corrective maintenance. To minimize the unnecessary costs, a more active maintenance policy based on the life expectancy of the key components is preferred. Optimal scheduling of preventive maintenance activities requires advanced mathematical modeling. In this paper, an optimization model is developed using the renewal-reward theorem. In the multi-component setting, our approach involves a new idea of virtual maintenance which allows us to treat each replacement event as a renewal event even if some components are not replaced by new ones. The proposed optimization algorithm is applied to a four-component model of a wind turbine and the optimal maintenance plans are computed for various initial conditions. The modeling results showed clearly the benefit of PM planning compared to pure CM strategy (about 8.5% lower maintenance cost). When we compare it with another state-of-art optimization model, it shows similar scheduling with a much faster CPU time. The comparison demonstrated that our model is both fast and accurate.

math.OC↗

arXiv2020

Optimal scheduling of the next preventive maintenance activity for a wind farm

A large part of the operational cost for a wind power farm is due to the cost of equipment maintenance, especially for offshore wind farms. How to reduce the maintenance cost, and hence increase profitability, is this article's focus. It presents a binary linear optimization model whose solution may suggest the wind turbine owners which components, and when, should undergo the next preventive maintenance (PM) replacements. The suggested short-term scheduling strategy takes into account eventual failure events of the multi-component system, in that after the failed system is repaired, the previously scheduled PM plan should be updated, assuming that the restored components are as good as new. The optimization algorithm of this paper, NextPM, is tested through numerical case studies applied to a four component model of a wind turbine. The first study addresses the important case of a single component system, used for parameter calibration purposes. The second study analyses the case of seasonal variations of mobilization costs, as compared to the constant mobilization cost setting. Among other things, this analysis reveals a 35% cost reduction achieved by the NextPM model, as compared to the pure corrective maintenance (CM) strategy. %In these two case studies, the costs are reduced by around. The third case study compares the NextPM model with another optimization model - the preventive maintenance scheduling problem with interval costs (PMSPIC), which was the major source of inspiration for this article. This comparison demonstrates that the NextPM model is accurate and much faster in terms of computational time.

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