Maintenance models applied to wind turbines. A comprehensive overview

Producción CientíficaWind power generation has been the fastest-growing energy alternative in recent years, however, it still has to compete with cheaper fossil energy sources. This is one of the motivations to constantly improve the efficiency of wind turbines and develop new Operation and Maintenance (O&M) methodologies. The decisions regarding O&M are based on different types of models, which cover a wide range of scenarios and variables and share the same goal, which is to minimize the Cost of Energy (COE) and maximize the profitability of a wind farm (WF). In this context, this review aims to identify and classify, from a comprehensive perspective, the different types of models used at the strategic, tactical, and operational decision levels of wind turbine maintenance, emphasizing mathematical models (MatMs). The investigation allows the conclusion that even though the evolution of the models and methodologies is ongoing, decision making in all the areas of the wind industry is currently based on artificial intelligence and machine learning models

A review of asset management literature on multi-asset systems

This article gives an overview of the literature on asset management for multi-unit systems with an emphasis on two multi-asset categories: fleet (a system of homogeneous assets) and portfolio (a system of heterogeneous assets). As asset systems become more complicated, researchers have employed different terms to refer to their specific problems. With an objective to facilitate readers in searching conducive studies to their interests, this paper establishes a novel classification scheme for multi-unit systems in accordance with essential features such as diversity of assets and intervention options. Moreover, discerning differences in characteristics between cross-component and cross-asset interactions, we select three types of potential multi-component dependencies (performance, stochastic, and resource) and extend their notions to be applicable to multi-asset systems. The investigation into these dependencies enables the identification of problems that could exist in real industrial settings but are yet to be determined in academia. Ultimately, we delve into modelling approaches adopted by previous researchers. This comprehensive information allows us to offer the insights into the current trends in multi-asset maintenance. We expect that the output of this review paper will not only stress research gaps on multi-asset systems, but more importantly help systematise future studies on this aspect

RELIABILITY EVALUATION OF A WIND INTEGRATED POWER SYSTEM WITH COMPRESSED AIR ENERGY STORAGE

World-wide environmental concerns about green-house gas emissions from conventional generation sources have led to an increase in renewable energy penetration in electric power systems. Wind energy as a form of renewable power generation is environmentally friendly and suitable for bulk power generation. Wind power sources however, are intermittent and stochastic in nature and their increased penetration in the electric power system will introduce major challenges to reliable planning and operation of electric power systems. Energy storage systems are receiving considerable attention as potential means to adequately harness the benefits from wind power by absorbing the variability and reducing or eliminating the uncertainty in renewable power generation. This thesis is focused on compressed air energy storage (CAES) which has a high potential to be used on a grid scale. The ability of the CAES to absorb the variability and mitigate the uncertainty associated with wind energy is explored. The development of a suitable reliability model for the operation of the CAES is presented which proposes a hybrid approach by integrating a Monte Carlo Simulation (MCS) method with an analytical technique. The MCS technique is used to model the state of charge (SOC) of the CAES during the charging operation while recognizing the time chronology and the correlation between the variation in the wind, the load and the SOC of the storage. The analytical technique utilizes a period analysis to quantitatively assess the system adequacy for the diurnal and seasonal sub-periods under consideration. The diurnal analysis with sub-periods within a day captures the operation of the CAES on a daily cycle. The assumption is made that a seasonal period consists of a number of days with similar diurnal profile. This thesis presents the reliability and economic benefits of CAES being utilized in a number of ways to meet different objectives. The CAES can be operated in coordination with the wind resources to absorb the variability of wind power to promote renewable energy utilization in the system. A merchant owned CAES operated in an electricity market tries to exploit short term price difference to maximize profit from energy arbitrage. Different scenarios and operating strategies are used to investigate these objectives in this thesis. An energy management strategy for an annual study is developed and tested using appropriate data. The strategy divides the year into different seasons, and low cost energy is transferred from the off-peak season to the peak season. The effect of energy management is examined with respect to monetary profit and reliability improvement. Results obtained in this thesis and the conclusions drawn can be a valuable source of information to help utilities in effective and efficient planning of their systems considering CAES

Predictive Group Maintenance Model for Networks of Bridges

Recent progress in the monitoring and prediction of the condition of infrastructure using sensing technologies has motivated researchers and infrastructure owners to explore the benefits of asset predictive maintenance, as an alternative to reactive maintenance. However, the application of predictive group maintenance for multi-system multi-component networks (MSMCN) has not received much attention in the literature or in practice. The paper presents an approach that prioritizes the maintenance of MSMCN of bridges, using a deterioration model of components with uncertainty, a lifecycle cost model, a predictive model for the optimal time for maintenance based on the latest inspection, a group maintenance model to reduce setup cost, and a scheduling model considering budget constraints. This model has been applied to a network of 15 bridges constituted by multiple heterogeneous components, and, compared with the Structures Investment Toolkit, it showed potential for a substantial decrease in maintenance costs, thus highlighting the practical significance of the presented approach. EU H202

Genetic algorithms for condition-based maintenance optimization under uncertainty

International audienceThis paper proposes and compares different techniques for maintenance optimization based on Genetic Algorithms (GA), when the parameters of the maintenance model are affected by uncertainty and the fitness values are represented by Cumulative Distribution Functions (CDFs). The main issues addressed to tackle this problem are the development of a method to rank the uncertain fitness values, and the definition of a novel Pareto dominance concept. The GA-based methods are applied to a practical case study concerning the setting of a condition-based maintenance policy on the degrading nozzles of a gas turbine operated in an energy production plant

A review on maintenance optimization

To this day, continuous developments of technical systems and increasing reliance on equipment have resulted in a growing importance of effective maintenance activities. During the last couple of decades, a substantial amount of research has been carried out on this topic. In this study we review more than two hundred papers on maintenance modeling and optimization that have appeared in the period 2001 to 2018. We begin by describing terms commonly used in the modeling process. Then, in our classification, we first distinguish single-unit and multi-unit systems. Further sub-classification follows, based on the state space of the deterioration process modeled. Other features that we discuss in this review are discrete and continuous condition monitoring, inspection, replacement, repair, and the various types of dependencies that may exist between units within systems. We end with the main developments during the review period and with potential future research directions

Integration of maintenance optimization in process design and operation under uncertainty.

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