Arena-Based Modeling of the Maintenance Operation for a Wind Farm

2013 ◽  
Vol 401-403 ◽  
pp. 2205-2208 ◽  
Author(s):  
Huai Zhong Li ◽  
Tong Jing ◽  
Hong Zhang
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Discrete Event ◽  
Onshore Wind ◽  
Electric System ◽  
System A ◽  
Factors Influencing ◽  
Event Model ◽  
Main Factors ◽  
Discrete Event Model

Wind energy has become a leading developing direction in electric power. The high cost associated with turbine maintenance is a key challenging issue in wind farm operation as wind turbines are hard-to access for inspection and repair. Analysis of an onshore wind farm is carried out in this paper in terms of the operation, failure, and maintenance. Failures are categorized into three classes according to the downtime. It is found that the pitch, gearbox and generator have the most amount of downtime, while the most number of failures is from the pitch and electric system. A discrete-event model is developed by using Arena to simulate the operation, failure occurrence, and maintenance of the wind turbines, with an aim to determine the main factors influencing maintenance costs and the availability of the turbines in the wind farm.

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

SIMULATION ◽  
2021 ◽  
pp. 003754972110286
Author(s):  
Eduardo Pérez
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Computational Study ◽  
Scheduling Algorithm ◽  
Discrete Event ◽  
Wind Farms ◽  
Online Scheduling ◽  
Maintenance Scheduling ◽  
Lead Times ◽  
System Specification

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.

scholarly journals A discrete event model of clinical trial enrollment at eli lilly and company

2007 ◽  
Author(s):  
Bernard M. McGarvey ◽  
Nancy J. Dynes ◽  
Burch C. Lin ◽  
Wesley H. Anderson ◽  
James P. Kremidas ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Discrete Event ◽  
Event Model ◽  
Clinical Trial Enrollment ◽  
Discrete Event Model

scholarly journals Validation of a Stochastic Discrete Event Model Predicting Virus Concentration on Nurse Hands

Risk Analysis ◽  
2019 ◽  
Vol 39 (8) ◽  
pp. 1812-1824 ◽  
Author(s):  
Amanda M. Wilson ◽  
Kelly A. Reynolds ◽  
Marc P. Verhougstraete ◽  
Robert A. Canales
Keyword(s):  
Discrete Event ◽  
Virus Concentration ◽  
Event Model ◽  
Discrete Event Model

scholarly journals Approaches to Modeling the Gas-Turbine Maintenance Process

2011 ◽  
Vol 12 (1) ◽  
Author(s):  
Tai-Tuck Yu ◽  
James P. Scanlan ◽  
Richard M. Crowder ◽  
Gary B. Wills
Keyword(s):  
Decision Making ◽  
Gas Turbine ◽  
Discrete Event ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Event Modeling ◽  
Human Decision ◽  
Event Model ◽  
Discrete Event Model

Discrete-event modeling has long been used for logistics and scheduling problems, while multi-agent modeling closely matches human decision-making process. In this paper, a metric-based comparison between the traditional discrete-event and the emerging agent-based modeling approaches is reported. The case study involved the implementation of two functionally identical models based on a realistic, nontrivial, civil aircraft gas turbine global repair operation. The size, structural complexity, and coupling metrics from the two models were used to gauge the benefits and drawbacks of each modeling paradigm. The agent-based model was significantly better than the discrete-event model in terms of execution times, scalability, understandability, modifiability, and structural flexibility. In contrast, and importantly in an engineering context, the discrete-event model guaranteed predictable and repeatable results and was comparatively easy to test because of its single-threaded operation. However, neither modeling approach on its own possesses all these characteristics nor can each handle the wide range of resolutions and scales frequently encountered in problems exemplified by the case study scenario. It is recognized that agent-based modeling can emulate high-level human decision-making and communication closely while discrete-event modeling provides a good fit for low-level sequential processes such as those found in manufacturing and logistics.

scholarly journals AGN variability time scales and the discrete-event model

2005 ◽  
Vol 443 (2) ◽  
pp. 451-463 ◽  
Author(s):  
P. Favre ◽  
T. J.-L. Courvoisier ◽  
S. Paltani
Keyword(s):  
Time Scales ◽  
Discrete Event ◽  
Event Model ◽  
Discrete Event Model

scholarly journals Influence of Wind Turbines on Farmlands’ Value: Exploring the Behaviour of a Rural Community through the Decision Tree

2021 ◽  
Vol 13 (17) ◽  
pp. 9630
Author(s):  
Giovanni Ottomano Palmisano ◽  
Annalisa De Boni ◽  
Rocco Roma ◽  
Claudio Acciani
Keyword(s):  
Rural Communities ◽  
Decision Tree ◽  
Rural Community ◽  
Wind Turbines ◽  
Rural Areas ◽  
Wind Farm ◽  
Wind Farms ◽  
Market Value ◽  
Factors Affecting ◽  
Main Factors

The relationship between wind energy and rural areas leads to the controversial debate on the effects declared by rural communities after wind farms or single turbines are operative. The literature on this topic lacks dedicated studies analysing how the behaviour of rural communities towards wind turbines can affect the market value of farmlands. This research aims to examine to the extent to which the easement of wind turbines can influence the market value of farmlands in terms of willingness to pay (WTP) by a small rural community, and to identify the main factors affecting the WTP. Starting from data collected via face-to-face interviews, a decision tree is then applied to investigate the WTP for seven types of farmland in a rural town of Puglia Region (Southern Italy) hosting a wind farm. Results of the interviews show a broad acceptance of the wind farm, while the decision tree classification shows a significant reduction of WTP for all farmlands. The main factors influencing the WTP are the education level, the possibility to increase the income, the concerns for impacts on human health and for maintenance workmen. National and local policy measures have to be put in place to inform rural communities about the ‘magnitude’ of the effects they identified as crucial, so that policy-makers and private bodies will contribute to make the farmland market more equitable.

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