Intuitionistic fuzzy model for reliability management in wind turbine system

Aikhuele, Daniel O.

doi:10.1016/j.aci.2018.05.003

Cited by 11 publications

(11 citation statements)

References 32 publications

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“…After finding these, we have to calculate the integrated utility of each alternative using (12). The weight of each criterion is assigned by the CRITIC method.…”

Section: The Nwhf-maut Methodsmentioning

confidence: 99%

“…In developing countries such as China, low-cost wind power generation has been studied. In summary, wind farm performance is ranked with the AHP method (Lee et al, 2009) [10]; meanwhile, to remove ambiguity in critical situations for decision-makers in their analysis of windmill alternatives, fuzzy set theory, Intuitionistic fuzzy set theory, and Interval-valued intuitionistic fuzzy set theory are used (Onar et al, 2015) [11] (Aikhude, 2018) [12].…”

Section: Introductionmentioning

confidence: 99%

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A new extension of hesitant fuzzy set: An application to an offshore wind turbine technology selection process

Narayanamoorthy

Ramya

Băleanu

et al. 2021

IET Renewable Power Gen

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Wind energy is an energy source that is naturally clean, safe and cheap. It comes from a variety of sources. The electric energy generated by a wind turbine manifests as kinetic energy throughout the earth. The energy received from the wind is clean and is permanently available and can be generated forever. Turbine characteristics also have an impact on wind energy production. The turbine properties within a wind farm are important in estimating the load on power generation and wind turbine energy. The amount of energy released is calculated according to the type of the turbine model applied. In many situations, the choices of turbine model can incur various vague and complicated hesitation situations. To manage this situation, a hesitant fuzzy set with the Multi Criteria Decision Making (MCDM) is used. In the present research, the newly proposed Normal Wiggly Hesitant Fuzzy-Criteria Importance Through Intercriteria Correlation (NWHF-CRITIC) and Normal Wiggly Hesitant Fuzzy-Multi Attribute Utility Theory (NWHF-MAUT) methods were employed to rank turbine models based on quality, power level, voltage, and capacity. As part of this process, the NWHF method was utilized to extract and gather deeper information from the decision-makers. 1This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…After finding these, we have to calculate the integrated utility of each alternative using (12). The weight of each criterion is assigned by the CRITIC method.…”

Section: The Nwhf-maut Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new extension of hesitant fuzzy set: An application to an offshore wind turbine technology selection process

Narayanamoorthy

Ramya

Băleanu

et al. 2021

IET Renewable Power Gen

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“…A key requirement of reliability management in wind turbines is the task of a prior detection of failures. This is necessary to ensure reliable continuous operation and maintenance of wind turbine machine systems (Kusiak & Li, 2011;Aikhuele & Turan, 2016, 2018. A priori failure detection in wind turbine is a prerequisite for ensuring failure prediction maintenance (Nivedh, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Fault estimation and failure detection can be described as a decision-making process in many fields of discipline, this includes engineering (Aikhuele, 2018;Su & Hu, 2018). Fault and failure detection, therefore, provides advanced information about a system; this process helps in the identification and description of the intensity of a system behavior.…”

Section: Introductionmentioning

confidence: 99%

Wind turbine systems operational state and reliability evaluation: An artificial neural network approach

Aikhuele¹,

Periola²,

Ighravwe³

2019

10.5267/j.ijdns

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The increased role of wind turbine systems makes it important for its operational states to be continuously monitored and optimized. This goal can be achieved using existing methods, which relies on closed-form expressions. The use of these methods, however, becomes challenging when interacting parameters cannot be fully presented with closed form expressions. In this paper, an artificial neural network (ANN) based algorithm is proposed as a solution to this problem. This algorithm is used to estimate wind turbine systems operational state and reliability. The proposed method is able to provide a more holistic approach to manage a wind turbine system with respect to the problem mentioned above. Simulation results show that the developed ANN can predict the average number of failures per year, distribution of failure and average downtime per failure with good accuracy. This was achieved using an ANN model with 5-15-3 architecture. The model generates mean square errors of 4.6 × 10-3 , 4.2 ×10-3 , and 4.0 × 10-3 at the training, validation, and testing stages, respectively. The study is beneficial to wind turbine practitioners and manufacturers as its findings can provide in-depth understandings of reliability issues of the system.

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“…Due to the fact that different power electronic devices have different failure rates, the MTTF of a converter must be calculated based on a static value. As reported in literature, the reliability of a component is associated with uncertainties that originate from manufacturing technology, design uncertainty and lack of sufficient knowledge of physics of failure [25][26][27]. Regarding the mentioned fact and knowing that the failure rates of components and fault-coverage-mechanism probability are not pre-defined values, fuzzy-based reliability analysis can be a suitable technique in common systems architecture [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Logic-Based Mean Time to Failure (MTTF) Analysis of Interleaved Dc-Dc Converters Equipped with Redundant-Switch Configuration

et al. 2018

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Interleaved dc-dc converters in sensitive applications necessitate an enhanced reliability. An interleaved converter equipped with redundant components can fulfill the reliability requirements. Mean Time to Failure (MTTF), as a reliability index, can be used to evaluate the expected life span of the mentioned converters. The Markov model is a helpful tool to calculate the MTTF in such systems. Different scientific reports denote different failure rates with different weight for power elements. Also, in reliability reports, failure rates of active and passive components are uncertain values. In order to approximate the failure rates fuzzy-logic-based Markov models are proposed in this paper. Then it is used to evaluate the MTTF of an interleaved multi-phase dc-dc converter, which is equipped with parallel and standby switch configurations. For the first time, fuzzy curves for MTTFs of the converters and 3D reliability function are derived in this paper. The reliability analyses give an insight to find the appropriate redundant-switch configurations for interleaved dc-dc converters under different conditions. Simulation and experimental results are provided to lend credence to the viability of the studied redundant-switch configurations in interleaved dc-dc boost converter.

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Intuitionistic fuzzy model for reliability management in wind turbine system

Cited by 11 publications

References 32 publications

A new extension of hesitant fuzzy set: An application to an offshore wind turbine technology selection process

A new extension of hesitant fuzzy set: An application to an offshore wind turbine technology selection process

Wind turbine systems operational state and reliability evaluation: An artificial neural network approach

Fuzzy-Logic-Based Mean Time to Failure (MTTF) Analysis of Interleaved Dc-Dc Converters Equipped with Redundant-Switch Configuration

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