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Register a new userOptimal preventive maintenance scheduling for wind turbines under condition monitoring
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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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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.
Condition based maintenance is a modern approach to maintenance which has been successfully used in several industrial sectors. In this paper we present a concrete statistical approach to condition based maintenance for wind turbine by applying ideas from statistical process control. A specific problem in wind turbine maintenance is that failures of a certain part may have causes that originate in other parts a long time ago. This calls for methods that can produce timely warnings by combining sensor data from different sources. Our method improves on existing methods used in wind turbine maintenance by using adaptive alarm thresholds for the monitored parameters that correct for values of other relevant parameters. We illustrate our method with a case study that shows that our method is able to predict upcoming failures much earlier than currently used methods.
Motivated by original equipment manufacturer (OEM) service and maintenance practices we consider a single component subject to replacements at failure instances and two types of preventive maintenance opportunities: scheduled, which occur due to periodic system reviews of the equipment, and unscheduled, which occur due to failures of other components in the system. Modelling the state of the component appropriately and incorporating a realistic cost structure for corrective maintenance as well as condition-based maintenance (CBM), we derive the optimal CBM policy. In particular, we show that the optimal long-run average cost policy for the model at hand is a control-limit policy, where the control limit depends on the time until the next scheduled opportunity. Furthermore, we explicitly calculate the long-run average cost for any given control-limit time dependent policy and compare various policies numerically.
In approachability with full monitoring there are two types of conditions that are known to be equivalent for convex sets: a primal and a dual condition. The primal one is of the form: a set C is approachable if and only all containing half-spaces are approachable in the one-shot game; while the dual one is of the form: a convex set C is approachable if and only if it intersects all payoff sets of a certain form. We consider approachability in games with partial monitoring. In previous works (Perchet 2011; Mannor et al. 2011) we provided a dual characterization of approachable convex sets; we also exhibited efficient strategies in the case where C is a polytope. In this paper we provide primal conditions on a convex set to be approachable with partial monitoring. They depend on a modified reward function and lead to approachability strategies, based on modified payoff functions, that proceed by projections similarly to Blackwells (1956) strategy; this is in contrast with previously studied strategies in this context that relied mostly on the signaling structure and aimed at estimating well the distributions of the signals received. Our results generalize classical results by Kohlberg 1975 (see also Mertens et al. 1994) and apply to games with arbitrary signaling structure as well as to arbitrary convex sets.
It is likely that electricity storage will play a significant role in the balancing of future energy systems. A major challenge is then that of how to assess the contribution of storage to capacity adequacy, i.e. to the ability of such systems to meet demand. This requires an understanding of how to optimally schedule multiple storage facilities. The present paper studies this problem in the cases where the objective is the minimisation of expected energy unserved (EEU) and also a form of weighted EEU in which the unit cost of unserved energy is higher at higher levels of unmet demand. We also study how the contributions of individual stores may be identified for the purposes of their inclusion in electricity capacity markets.
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