Reliability optimization of wind farms considering redundancy and opportunistic maintenance strategy

Atashgar, Karim; Abdollahzadeh, Hadi

doi:10.1016/j.enconman.2016.01.027

Cited by 59 publications

(21 citation statements)

References 35 publications

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“…The resulting procedure is tested-with good results-on instances adapted from the literature. Atashgar and Abdollahzadeh (2016) formulate a joint redundancy and imperfect block opportunistic maintenance optimization model, to determine the redundancy level and a maintenance strategy that simultaneously minimizes the wind farm loss-of-load probability and life-cycle cost. Reliability thresholds are introduced for imperfect maintenance of failed and working turbines, and preventive dispatch of maintenance teams.…”

Section: Literature Surveymentioning

confidence: 99%

The stochastic opportunistic replacement problem, part III: improved bounding procedures

2019

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We consider the problem to find a schedule for component replacement in a multi-component system, whose components possess stochastic lives and economic dependencies, such that the expected costs for maintenance during a pre-defined time period are minimized. The problem was considered in Patriksson et al. (Ann Oper Res 224:51-75, 2015b), in which a two-stage approximation of the problem was optimized through decomposition (denoted the optimization policy). The current paper improves the effectiveness of the decomposition approach by establishing a tighter bound on the value of the recourse function (i.e., the second stage in the approximation). A general lower bound on the expected maintenance cost is also established. Numerical experiments with 100 simulation scenarios for each of four test instances show that the tighter bound yields a decomposition generating fewer optimality cuts. They also illustrate the quality of the lower bound. Contrary to results presented earlier, an age-based policy performs on par with the optimization policy, although most simple policies perform worse than the optimization policy.

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Section: Literature Surveymentioning

confidence: 99%

The stochastic opportunistic replacement problem, part III: improved bounding procedures

2019

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“…• failure probability thresholds Joint redundancy and maintenance Few researchers have considered the third category of studies introduced previously. Levitin and Lisnianski, 26 De Smidt-Destombes et al, 27,28 Moghaddass et al, 29 Nourelfath et al, 30 Liu et al, 31 Chen et al, 32 Xiao and Peng, 33 and Atashgar and Abdollahzadeh 34 have provided their researches based on the third approach. Table I summarizes the assumptions and features of the studies reviewed in this paper.…”

Section: Joint Redundancy and Maintenancementioning

confidence: 99%

A Joint Reliability and Imperfect Opportunistic Maintenance Optimization for a Multi‐State Weighted k‐out‐of‐n System Considering Economic Dependence and Periodic Inspection

Atashgar

Abdollahzadeh

2017

Quality & Reliability Eng

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This paper formulates a model to simultaneously optimize the redundancy and imperfect opportunistic maintenance of a multi‐state weighted k‐out‐of‐n system. Different from existing approaches that consider binary or multi‐state elements, our approach considers modular redundancy in which each module/subsystem is composed of several multi‐state components in series. The status of each component is considered to degrade with use. Therefore, a new condition‐based opportunistic maintenance approach using three different thresholds for a component health state is developed. The objective is to determine 1) the minimal‐cost of k‐out‐of‐n system structure, 2) optimal imperfect opportunistic maintenance strategy, 3) optimal maintenance capacity, and 4) optimal inspection interval subject to an availability constraint. System availability is defined as the ability to satisfy consumer demand. Based on the three‐phase approach, a simulation procedure is used to evaluate the expected multi‐state system availability and life cycle costs. Also, a multi‐seed Tabu search heuristic algorithm with a proper neighborhood generation mechanism is proposed to solve the formulated problem. An application to the optimal design of a wind farm is provided to illustrate the proposed approach. Sensitivity analysis is conducted to discuss the influence of the different parameters of the simulation model. Copyright © 2017 John Wiley & Sons, Ltd.

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“…The opportunistic maintenance is also proposed with different types of maintenance actions, e.g., the works in [34,[45][46][47]. In addition, the authors of [48,49] integrate the opportunistic maintenance policy with multi-objective optimization.…”

Section: Background and Introductionmentioning

confidence: 99%

Framework for Managing Maintenance of Wind Farms Based on a Clustering Approach and Dynamic Opportunistic Maintenance

et al. 2019

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Energies 2019, 12, 2036 2 of 17 account for up to one quarter of the proportion of the LCoE, with 80% of this expenses directly attributed to maintenance [10][11][12].In view of the maintenance role in the LCoE and thus in the profitability of WFs, it is important to consider models for optimization of O&M plans and decisions [2]. The evolution of maintenance models and methodologies have kept pace with the constant technological evolution of Wind Turbines (WTs) [3]. According to [3], the goal of all the approaches and methodologies is determining the most adequate maintenance plan, the management of the resources, and the aspects related to Reliability, Availability, and Maintainability (RAM) of the WTs.Within such context, the WF operators are bound to develop new techniques and decision-support tools for optimal maintenance strategies, if they strive to maximize the profitability of the investment [8]. Accordingly, maintenance management discipline acquires a highly significant position since it provides a comprehensive perspective for the management of WTs, allowing for optimal maintenance strategies which reduce maintenance costs while maximizing availability [3,5].The decision-making process in the asset management field has been divided into strategic (long-term), tactic (medium-term), and operational (short-term) to achieve excellence in maintenance [13][14][15]. It is the purpose of the research in this paper to address the different aspects of the maintenance decision-making process. This is done by answering the research question of whether it is possible to achieve maintenance excellence for the lifecycle of WFs by a maintenance strategy which considers the different behaviors of the WTs and integrated business-related objectives.Aiming at providing an answer for the aforementioned research question, a technical framework for managing maintenance is proposed in the paper. The framework is a comprehensive proposal that considers different aspects regarding the maintenance of a WF. Within the possibilities offered by the current trend for big data, certain key aspects to create a failure database are integrated in the framework, which enables a clustering approach based on the failure behaviors of the different failure modes of each WT. This approach supports an opportunistic maintenance policy with dynamic thresholds that regard not only to the reliability of the assets but also business considerations. Besides, the framework also incorporates the strategic view through a Life-Cycle Cost (LCC) perspective integrated by means of a multi-objective optimization and supported by simulation techniques that provide valuable information to find an attractive trade-off between cost and performance.

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Reliability optimization of wind farms considering redundancy and opportunistic maintenance strategy

Cited by 59 publications

References 35 publications

The stochastic opportunistic replacement problem, part III: improved bounding procedures

The stochastic opportunistic replacement problem, part III: improved bounding procedures

A Joint Reliability and Imperfect Opportunistic Maintenance Optimization for a Multi‐State Weighted k‐out‐of‐n System Considering Economic Dependence and Periodic Inspection

Framework for Managing Maintenance of Wind Farms Based on a Clustering Approach and Dynamic Opportunistic Maintenance

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