Selection of appropriate defuzzification methods: Application to the assessment of dam performance

Wind 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.

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“…Basic scheme of a fuzzy inference system and models applied in each stage. Source [11,[189][190][191], own elaboration.…”

Section: Fuzzy Logicmentioning

confidence: 99%

Maintenance Models Applied to Wind Turbines. A Comprehensive Overview

et al. 2019

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“…Models must be adapted for uncertain situations. Methods such as Bayesian networks (Simon & Weber, ), multicriteria aggregation (Tacnet, Dezert, Curt, Batton‐Hubert, & Chojnacki, ), and knowledge‐based systems (Curt, Talon, & Mauris, ; Talon & Curt, ) can be used to propagate these imperfections in decision models. Obviously, the spatial aspect is important for network infrastructures and because resilience management is generally performed at territorial level.…”

Section: Limits Linked To Implementing Resilience Control Systemsmentioning

confidence: 99%

Resilience of Critical Infrastructures: Review and Analysis of Current Approaches

Curt¹,

Tacnet

2018

Risk Analysis

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In crisis situations, systems, organizations, and people must react and deal with events that are inherently unpredictable before they occur: vital societal functions and thus infrastructures must be restored or adapted as quickly as possible. This capacity refers to resilience. Progress concerning its conceptualization has been made but it remains difficult to assess and apply in practice. The results of this article stem from a literature review allowing the analysis of current advances in the development of proposals to improve the management of infrastructure resilience. The article: (i) identifies different dimensions of resilience; (ii) highlights current limits of assessing and controlling resilience; and (iii) proposes several directions for future research that could go beyond the current limits of resilience management, but subject to compliance with a number of constraints. These constraints are taking into account different hazards, cascade effects, and uncertain conditions, dealing with technical, organizational, economical, and human domains, and integrating temporal and spatial aspects.

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“…Fuzzification is the operation that can translate mathematical input value into the membership function of Fuzzy Logic [8], [15]. On the other hand, De-fuzzification is the operation that converts the output from Fuzzy Logic membership function into the value that can be used by the controller [16,17]. The application of Fuzzy Logic Controller for aerial vehicle is not well investigated.…”

Section: Introductionmentioning

confidence: 99%

Design of travel angle control of quanser bench-top helicopter using mamdani-based fuzzy logic controller

Mansor

Esa

Gunawan

et al. 2020

IJEECS

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This research focuses on travel angle control of a laboratory scale bench-top helicopter developed by Quanser Inc. Bench top-helicopter is usually used by engineers and researchers to test their designed controllers before applying to the actual helicopter. Bench-top helicopter has the same behavior as the real helicopter, with 3 degree of freedom. The bench-top helicopter is mounted on a flat surface with two rotors that depends on the voltage supplied to change the direction of the helicopter in 3 different angles. The movement of the helicopter is based on the direction of three-different angles; travel, pitch and yaw angles. The existing Linear Quadratic Regulator-Integral controller used by Quanser Inc has some limitations in terms of tracking capability and settling time; therefore, this research is proposed. The objective of this research is to develop Mamdani-based Fuzzy Logic Controller for travel angle control of bench-top helicopter. Performance comparison has been done with the existing Linear Quadratic Regulator-Integral controller in both simulation and hardware. From the test results, it was found that the performance of Fuzzy Logic Controller is better than LQR-I controller especially for closed-loop simulation at desired angle of 30°. The percentage of overshoot of the Fuzzy Logic Controller has been improved from the existing controller which is 4.912% compared to 7.002% for LQR-I. Keywords:Bench-top helicopter Linear quadratic integral controller Mamdani-based fuzzy logic controller Travel angle control

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Selection of appropriate defuzzification methods: Application to the assessment of dam performance

Cited by 60 publications

References 36 publications

Maintenance Models Applied to Wind Turbines. A Comprehensive Overview

Maintenance Models Applied to Wind Turbines. A Comprehensive Overview

Resilience of Critical Infrastructures: Review and Analysis of Current Approaches

Design of travel angle control of quanser bench-top helicopter using mamdani-based fuzzy logic controller

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