Multi-objective opportunistic maintenance optimization of a wind farm considering limited number of maintenance groups

Abdollahzadeh, Hadi; Atashgar, Karim; Abbasi, Morteza

doi:10.1016/j.renene.2015.11.022

Cited by 93 publications

(54 citation statements)

References 23 publications

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“…The nonlinear term λ 4 h,g,b,s q h,g,s in Equation (21) can also be simplified through Equation (9). In addition, by considering Equation (12), Equation (21) becomes:…”

Section: Karush-kuhn-tucker Conditionsmentioning

confidence: 99%

“…Especially, in the deregulated power system where the electricity prices vary constantly, finding the optimum maintenance window is a difficult task. There are several models focused on preventive maintenance scheduling of components in a wind turbine (WT) through failure rate and age modeling [9][10][11][12]. Apart from the uncertainty in input data, the uncertainty in failure rate, age or degradation models and the corresponding threshold values are the most important parameters that influence the performance of such models [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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Strategic Maintenance Scheduling of an Offshore Wind Farm in a Deregulated Power System

2017

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This paper proposes a model for strategic maintenance scheduling of offshore wind farms (SMSOWF) in a deregulated power system. The objective of the model is to plan the maintenance schedules in a way to maximize the profit of the offshore wind farm. In addition, some network constraints, such as transmission lines capacity, and wind farm constraints, such as labor working shift, wave height limit and wake effect, as well as unexpected outages, are included in deterministic and stochastic studies. Moreover, the proposed model provides the ability to incorporate information from condition monitoring systems. SMSOWF is formulated through a bi-level formulation and then transformed into a single-level through Karush-Kuhn-Tucker conditions. The model is validated through a test system, and the results demonstrate applicability, advantages and challenges of harnessing the full potential of the model.

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“…The nonlinear term λ 4 h,g,b,s q h,g,s in Equation (21) can also be simplified through Equation (9). In addition, by considering Equation (12), Equation (21) becomes:…”

Section: Karush-kuhn-tucker Conditionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Strategic Maintenance Scheduling of an Offshore Wind Farm in a Deregulated Power System

2017

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“…Ding and Tian reported an opportunistic maintenance policy based on the component's age threshold. Abdollahzadeh et al . introduced a bi‐objective opportunistic maintenance optimization model considering component reliability thresholds.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In these studies, the researchers present their models based on the assumption of perfect maintenance actions. In 4 Seriesk-out-of-n • • Boddu and Xing 5 Seriesk-out-of-n • • Xie et al 6 k-out-of-n • • Sharma and Misra 7 Seriesk-out-of-n • • Fawzi and Hawkes 8 k-out-of-n • • Wang 9 k-out-of-n • • Gurov et al 10 Seriesk-out-of-n • • Khatab et al 11 k-out-of-n • • Yuan 12 k-out-of-n • • Moghaddass et al 13 k-out-of-n • • Ruiz-Castro and Li 14 k-out-of-n • • Maintenance optimization Pham and Wang 17 k-out-of-n • • Krishnamoorthy et al 18 k-out-of-n 22 k-out-of-n • • Abdollahzadeh et al 23 Weighted k-out-of-n • • Tian et al 24 k-out-of-n • •…”

Section: Literature Reviewmentioning

confidence: 99%

“…Ding and Tian 22 reported an opportunistic maintenance policy based on the component's age threshold. Abdollahzadeh et al 23 introduced a bi-objective opportunistic maintenance optimization model considering component reliability thresholds. The maintenance policies introduced previously are proposed based on life distribution of system components without consideration of current health state of components.…”

Section: Joint Redundancy and Maintenancementioning

confidence: 99%

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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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Simulation Multi‐Objective Particle Swarm Optimization of a Crude Oil Distillation Unit

2022

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The crude distillation unit (CDU) plays a significant role in the petroleum refinery. Decision variables of this unit have an effect on the final product quality, quantity, and energy consumption. The aim of this work is to increase the profit of the refinery proportionally with reducing emissions by getting high distillate products through selection of the optimal conditions. Two goal functions in three cases, namely, profit, CO2 emission, energy cost, and total distillate are considered. Therefore, the multi‐objective particle swarm optimization (MOPSO) algorithm is implemented by using MATLAB to optimize the CDU unit and then linked to an ASPEN HYSYS simulator. The effect of the temperature of preheated crude is demonstrated in the Pareto front. Also, the influence of the other variables, i.e., reflux ratio, atmospheric stripper steam, and amount of fuel burnt, is investigated.

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Multi-objective opportunistic maintenance optimization of a wind farm considering limited number of maintenance groups

Cited by 93 publications

References 23 publications

Strategic Maintenance Scheduling of an Offshore Wind Farm in a Deregulated Power System

Strategic Maintenance Scheduling of an Offshore Wind Farm in a Deregulated Power System

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

Simulation Multi‐Objective Particle Swarm Optimization of a Crude Oil Distillation Unit

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