A branch-and-check approach for a wind turbine maintenance scheduling problem

Froger, Aurélien; Gendreau, Michel; Mendoza, Jorge E.; Pinson, Éric; Rousseau, Louis-Martin

doi:10.1016/j.cor.2017.07.001

Cited by 19 publications

(11 citation statements)

References 21 publications

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“…Similarly, the model proposed by Froger et al 22 can only be solved for a time horizon of 20 timeunits. For the case presented in Froger et al, 31 the preset time limit to obtain a solution will be exceeded if the time horizon considered is higher than 5 days. In general, as stated in Grossmann et al, 32 the performance of commercially available solvers is limited when dealing with largescale MIP and NLIP models.…”

Section: Literature Reviewmentioning

confidence: 99%

A simulation-driven online scheduling algorithm for the maintenance and operation of wind farm systems

Pérez

2021

SIMULATION

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Wind turbines experience stochastic loading due to seasonal variations in wind speed and direction. These harsh operational conditions lead to failures of wind turbines, which are difficult to predict. Consequently, it is challenging to schedule maintenance actions that will avoid failures. In this article, a simulation-driven online maintenance scheduling algorithm for wind farm operational planning is derived. Online scheduling is a suitable framework for this problem since it integrates data that evolve over time into the maintenance scheduling decisions. The computational study presented in this article compares the performance of the simulation-driven online scheduling algorithm against two benchmark algorithms commonly used in practice: scheduled maintenance and condition-based monitoring maintenance. An existing discrete event system specification simulation model was used to test and study the benefits of the proposed algorithm. The computational study demonstrates the importance of avoiding over-simplistic assumptions when making maintenance decisions for wind farms. For instance, most literature assumes maintenance lead times are constant. The computational results show that allowing lead times to be adjusted in an online fashion improves the performance of wind farm operations in terms of the number of turbine failures, availability capacity, and power generation.

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Section: Literature Reviewmentioning

confidence: 99%

A simulation-driven online scheduling algorithm for the maintenance and operation of wind farm systems

Pérez

2021

SIMULATION

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“…This information has been used in previous research works to develop maintenance frameworks. For example, one study fixed the wind speed limit as 10 m/s for accessing the whole turbine [5], while another based the safe working limit on cut in wind speed, i.e., the turbine was only considered maintainable when the wind speed was lower than cut-in [6]. Furthermore, current regulations and maintenance guides include dynamic safety limits taking into account not only the mean 10-minute wind speed value but also the gust value, when a crane usage is required for such a case like major component replacement.…”

Section: Maintenance Plans and Problem Statementmentioning

confidence: 99%

“…When the constraints are investigated, wind farm accessibility normally comes in first place in terms of importance. Farm accessibility depends on the variability of the wind speed and the associated health-safety and environment regulations (HSE) [4][5][6][7][8][9]. In other words, finding an appropriate weather window is a major criterion for any type of intervention for wind turbines and there is a research need closing the gap between academic models and application in practice [10].…”

Section: Introductionmentioning

confidence: 99%

Automated wind turbine maintenance scheduling

Yürüşen

Rowley

Watson

et al. 2020

Reliability Engineering & System Safety

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While many operation and maintenance (O&M) decision support systems (DSS) have been already proposed, a serious research need still exists for wind farm O&M scheduling. O&M planning is a challenging task, as maintenance teams must follow specific procedures when performing their service, which requires working at height in adverse weather conditions. Here, an automated maintenance programming framework is proposed based on real case studies considering available wind speed and wind gust data. The methodology proposed consists on finding the optimal intervention time and the most effective execution order for maintenance tasks and was built on information from regular maintenance visit tasks and a corrective maintenance visit. The objective is to find possible schedules where all work orders can be performed without breaks, and to find out when to start in order to minimise revenue losses (i.e. doing maintenance when there is least wind). For the DSS, routine maintenance tasks are grouped using the findings of an agglomerative nesting analysis. Then, the task execution windows are searched within pre-planned maintenance day.

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“…In [18], offshore wind farms are also addressed, in this case finding the best routes for the crew transfer vessels. Conversely, the work [21] focus on on-shore wind farms and considers forecast wind-speed values, multiple task execution modes, and daily restrictions on the routes of the technicians to determine optimal maintenance operations scheduling.…”

Section: B Related Workmentioning

confidence: 99%

“…All these works tackle the question with different approaches, for instance [18] is based on the Large Neighbourhood Search meta-heuristic, whereas [21] adopts linear programming formulations and branch-and-check approach. In [20] the optimization is achieved simply through brute force whereas [17] adopts a hybrid optimization using first mixed particle swarm optimization to determine an optimal vessel allocation scheme and then discrete wolf pack search (DWPS) to optimize the maintenance route according to all constraints.…”

Section: B Related Workmentioning

confidence: 99%

Pick-up & Deliver in Maintenance Management of Renewable Energy Power Plants

Carchiolo

Francesco²,

Longheu

et al. 2020

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

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Logistic optimization is a strategic element in many industrial processes, given that an optimized logistics makes the processes more efficient. A relevant case, in which the optimization of logistics can be decisive, is the maintenance in a Wind Farm where it can lead directly to a saving of energy cost. Wind farm maintenance presents, in fact, significant logistical challenges. They are usually distributed throughout the territory and also located at considerable distances from each other, they are generally found in places far from uninhabited centers and sometimes difficult to reach and finally spare parts are rarely available on the site of the plant itself. In this paper, we will study the problem concerning the optimization of maintenance logistics of wind plants based on the use of specific vehicle routing optimization algorithms. In particular a pickup-anddelivery algorithm with time-window is adopted to satisfy the maintenance requests of these plants, reducing their management costs. The solution was applied to a case study in a renewable energy power plant. Results time reduction and simplification and optimization obtained in the real case are discussed to evaluate the effectiveness and efficiency of the adopted approach.

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A branch-and-check approach for a wind turbine maintenance scheduling problem

Cited by 19 publications

References 21 publications

A simulation-driven online scheduling algorithm for the maintenance and operation of wind farm systems

A simulation-driven online scheduling algorithm for the maintenance and operation of wind farm systems

Automated wind turbine maintenance scheduling

Pick-up & Deliver in Maintenance Management of Renewable Energy Power Plants

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