Micro-scale classification of offshore wind energy resource ——A case study of the New Zealand

Zheng, Chunmiao; Li, Chongyin; Xu, Jianjun

doi:10.1016/j.jclepro.2019.04.082

Cited by 24 publications

(6 citation statements)

References 27 publications

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“…The synthetic weights of RPN elements are defined as follows:

ω_{j} = θ_{2} ω_{j}^{o} + ((), 1 - θ_{2}) ω_{j}^{s},

where

ω_{j}^{s}

represents the subjective weights of RPN elements; θ 2 is the relative important degree of the objective weights

ω_{j}^{o}

. Note that subjective weights of RPN elements can be determined by many methods, such as analytic hierarchy process (AHP) method, least square method, and Delphi method 43,44 Establish the integrated rough cloud hierarchical matrix. The CWAO is employed to integrate all evaluation matrices given by FMEA team members to obtain an integrated rough cloud matrix.…”

Section: The Proposed Integrated Fmea Methodsmentioning

confidence: 99%

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An improving approach for failure mode and effect analysis under uncertainty environment: A case study of critical function component

Huang

Xiao

Zhang

et al. 2020

Quality & Reliability Eng

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Failure mode and effect analysis (FMEA) is a powerful risk discerning technique for identifying, evaluating, and reducing possible failures of products or processes. However, the classical FMEA has been criticized for inherent limitations, such as equal weights of risk elements and lack of capability in handling inaccurate information. Although fuzzy-based modified FMEA methods are frequently utilized to handle vagueness of experts' judgments, they still have some drawbacks, for example, requiring extra assumptions, neglecting experts' bounded rationality and psychological effects, lacking consideration of randomness, and only considering three classical risk elements among most of them. Therefore, this study develops an extended risk assessment method to enhance the performance of FMEA, which integrates the superiority of rough number theory in handling subjective and inaccurate information and the advantage of cloud model theory in reflecting the randomness of qualitative evaluations. Moreover, two synthetic weighting methods are developed to determine the weights of risk elements and handle the experts' individual effects, respectively, which consider both subjective and objective aspects. In addition, maintenance is added into the classical risk elements, and then a hierarchical structure containing four risk dimensions is built to evaluate failures' risk levels comprehensively. Finally, an application case to demonstrate the effectiveness of the developed FMEA model is presented. K E Y W O R D S cloud model, failure mode and effect analysis, hierarchical TOPSIS, rough number theory, synthetic weight 1 | INTRODUCTION Failure mode and effect analysis (FMEA) is a reliable risk assessment technique to determine the known and/or potential failure modes, causes, and effects on the stability and performance of a system, design, service, and process to identify the most crucial ones and conduct actions to eliminate them. 1 In the 1960s, it was firstly used by NASA in the aerospace industry to ensure the safety and quality of products. 2 FMEA is commonly performed by a cross-functional team contained experts with different knowledge and experience to analyze and quantify the relationships between

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“…The synthetic weights of RPN elements are defined as follows:

ω_{j} = θ_{2} ω_{j}^{o} + ((), 1 - θ_{2}) ω_{j}^{s},

where

ω_{j}^{s}

represents the subjective weights of RPN elements; θ 2 is the relative important degree of the objective weights

ω_{j}^{o}

Section: The Proposed Integrated Fmea Methodsmentioning

confidence: 99%

“…Note that subjective weights of RPN elements can be determined by many methods, such as analytic hierarchy process (AHP) method, least square method, and Delphi method. 43,44 Step 9: Establish the integrated rough cloud hierarchical matrix.…”

Section: Prioritize All Failure Modesmentioning

confidence: 99%

An improving approach for failure mode and effect analysis under uncertainty environment: A case study of critical function component

Huang

Xiao

Zhang

et al. 2020

Quality & Reliability Eng

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“…The effective wind speed occurrence (EWSO) implies that wind speed is sufficient for the production of wind energy resources and is classified as 5-25 m/ s [26][27][28][29]…”

Section: Methodology Of the Wind Data Assessmentmentioning

confidence: 99%

Wind energy resource assessment of Kütahya, Turkey using WAsP and layout optimization

Koşar

Özgür

2020

Proceedings of the Institution of Mechanical Engineers, Part A:

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Kütahya is considered as a candidate region for a wind farm investment due to Turkey's 2023 energy targets and its proximity to other wind farm investments. In this study, two years of wind data collected from a hill near the Evliya Çelebi Campus of Kütahya Dumlupınar University was used to evaluate the wind farm potential of Kütahya. First, the wind speed, wind direction, wind shear, turbulence intensity and wind speed ramp characteristics were determined. Second, the WAsP software was used to create a wind atlas for the region. Three sites with strong wind potential were evaluated. A techno-economic analysis was conducted using five types of wind turbines selected from the WAsP database. Third, optimization of a wind farm layout was conducted by considering different hub height options for 14 commercial wind turbines using MATLAB software. It was shown theoretically that a wind farm with a power capacity of 25 MW can operate with a capacity factor of 35%. However, due to the relatively high topographical ruggedness index on the wind farm site, the calculated value for the capacity factor could not be reached in a real-life application.

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“…However, the sources of information and research on the site selection project were not fully provided. Another study that concerns location selection decisions for renewable energy facilities was conducted by Zheng et al (2019). In particular, the micro‐scale classification method is used to evaluate locations and wind energy generating equipment in New Zealand.…”

Section: Literature Reviewmentioning

confidence: 99%

A prospect theory extension of data envelopment analysis model for wave‐wind energy site selection in New Zealand

Wang,

Nguyen,

Nhieu

et al. 2023

Manage Decis Econ

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Renewable energy sources have been utilized by countries worldwide, but offshore energy exploitation projects remain highly promising for both the world and New Zealand specifically. The selection of equipment installation locations can be challenging and requires significant investment, prompting the need for optimization in offshore energy exploitation. Wind and wave location (WWL) integrated sites offer a highly potential solution for reducing installation costs and enhancing efficiency in renewable energy exploitation. However, the decision‐making process for selecting equipment locations is crucial and directly affects the outcome. This study aims to evaluate and consider sustainable and effective equipment installation sites based on both quantitative indicators and behavioral psychology of decision making. To achieve this goal, the study utilizes the prospect theory Data Envelopment Analysis (PT‐DEA) model, which builds on PT to analyze and evaluate the effectiveness of offshore installation site selection with consideration for human psychology. By combining the results of quantitative indicators and decision‐making behavior, the study identifies sustainable and effective offshore installation sites, recommending appropriate equipment installation locations WWL. The study is also compared and referenced with relevant plans, projects, and development policies in New Zealand for confirmation.

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Micro-scale classification of offshore wind energy resource ——A case study of the New Zealand

Cited by 24 publications

References 27 publications

An improving approach for failure mode and effect analysis under uncertainty environment: A case study of critical function component

An improving approach for failure mode and effect analysis under uncertainty environment: A case study of critical function component

Wind energy resource assessment of Kütahya, Turkey using WAsP and layout optimization

A prospect theory extension of data envelopment analysis model for wave‐wind energy site selection in New Zealand

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