Selection of Wind Turbine Based on Fuzzy Analytic Network Process: A Case Study in China

Pang, Nansheng; Nan, Mengfan; Meng, Qichen; Zhao, Shuang

doi:10.3390/su13041792

Cited by 14 publications

(6 citation statements)

References 35 publications

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“…Innovative approaches in this field have incorporated fuzzy logic to allow for flexible decision-making rules in turbine selection [80], the application of SWARA methods to enhance decision quality in turbine selection [81], and the use of hybrid MCDM techniques combining the analytic network process (ANP) and entropy weight method (EWM) for turbine choice in specific offshore wind projects [82]. The integration of fuzzy preference programming has been explored to build sophisticated decision models for turbine selection [83,84]. In addition, D-number [85] and D-S evidence theory [86] are also introduced into the MCDM model for fan selection problems to improve decision-making performance.…”

Section: Turbine Selection For Offshore Wind Farmsmentioning

confidence: 99%

“…[82] Technology, adaptability to wind resources, economy impact, historical achievements, supplier services ANP-EWM Subjective ANP is combined with objective EWM to fully leverage their respective strengths in selecting wind turbines. [83] Reliability, economic impact, supplier services FPP-ANP Triangular fuzzy numbers and fuzzy comparison matrices are introduced, and fuzzy preference programming (FPP) is combined with the analytic network process to construct a fuzzy analytic network process (FANP) unit selection model. [85] Technical performance, adaptability to wind farm, economic impact, historical achievements, supplier services…”

Section: Swara-svns-topsismentioning

confidence: 99%

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Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

Song,

Shen,

Huang

et al. 2024

JMSE

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As global energy crises and climate change intensify, offshore wind energy, as a renewable energy source, is given more attention globally. The wind power generation system is fundamental in harnessing offshore wind energy, where the control and design significantly influence the power production performance and the production cost. As the scale of the wind power generation system expands, traditional methods are time-consuming and struggle to keep pace with the rapid development in wind power generation systems. In recent years, artificial intelligence technology has significantly increased in the research field of control and design of offshore wind power systems. In this paper, 135 highly relevant publications from mainstream databases are reviewed and systematically analyzed. On this basis, control problems for offshore wind power systems focus on wind turbine control and wind farm wake control, and design problems focus on wind turbine selection, layout optimization, and collection system design. For each field, the application of artificial intelligence technologies such as fuzzy logic, heuristic algorithms, deep learning, and reinforcement learning is comprehensively analyzed from the perspective of performing optimization. Finally, this report summarizes the status of current development in artificial intelligence technology concerning the control and design research of offshore wind power systems, and proposes potential future research trends and opportunities.

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Section: Turbine Selection For Offshore Wind Farmsmentioning

confidence: 99%

Section: Swara-svns-topsismentioning

confidence: 99%

Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

Song,

Shen,

Huang

et al. 2024

JMSE

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“…The evaluation set is the set of comprehensive evaluation results of the WT pitch system, and the number of elements in the evaluation set is related to the complexity of the evaluation process [21,22]. To obtain reasonable evaluation results, the evaluation set of three elements was selected as the evaluation set for the evaluation of the WT pitch system in rated power state and under power state.…”

Section: Establishing the Appropriate Evaluation Set And Evaluation C...mentioning

confidence: 99%

Operation State of the Wind Turbine Pitch System Based on Fuzzy Comprehensive Evaluation

Zhu¹,

Zhao²,

Gui³

et al. 2023

Energy Engineering

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To ensure wind turbine (WT) safe operations and improve the utilization rate of wind energy, effective evaluation of the operation state of the pitch system is critical. Therefore, a new method was proposed to evaluate the operation state of the pitch system of WT based on fuzzy comprehensive evaluation. Firstly, based on SCADA data, the working state of the pitch system under rated power state and power state of WT were analyzed. Secondly, through the analysis of characteristic parameters and physical mechanism of the pitch system, the consistency principle of characteristic parameters, the stability principle of power under rated state, and the stability principle of blade angle underpowered state were obtained. Next, based on the aforementioned principles, the evaluation indexes were established, and the fuzzy comprehensive evaluation method was used to establish the operation state evaluation model of the pitch system under rated power state and under power state of the WT. Finally, an example was provided to verify the effectiveness of the method. The evaluation model established in this study can be used as a technical reference for the online monitoring of WT pitch systems to ensure the safe and stable operation of WTs.

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“…The MCDM studies of wind turbine selection in the literature are quite limited (Supciller and Toprak, 2020). Studies related to wind turbine selection can be found in Supciller and Toprak (2020) and Pang et al (2021).…”

Section: Application: Wind Turbine Selectionmentioning

confidence: 99%

A Novel Z-Fuzzy AHP&EDAS Methodology and Its Application to Wind Turbine Selection

Tüysüz¹,

Kahraman²

2023

Informatica

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Modelling the reliability information in decision making process is an important issue to inclusively reflect the thoughts of decision makers. The Evaluation Based on Distance from Average Solution (EDAS) and Analytic Hierarchy Process (AHP) are frequently used MCDM methods, yet their fuzzy extensions in the literature are incapable of representing the reliability of experts’ fuzzy preferences, which may have important effects on the results. The first goal of this study is to extend the EDAS method by using Z-fuzzy numbers to reinforce its representation ability of fuzzy linguistic expressions. The second goal is to propose a decision making methodology for the solution of fuzzy MCDM problems by using Z-fuzzy AHP method for determining the criteria weights and Z-fuzzy EDAS method for the selection of the best alternative. The contribution of the study is to present an MCDM based decision support tool for the managers under vague and imprecise data, which also considers the reliability of these data. The applicability of the proposed model is presented with an application to wind energy investment problem aiming at the selection of the best wind turbine. Finally, the effectiveness and competitiveness of the proposed methodology is demonstrated by making a comparative analysis with the Z-fuzzy TOPSIS method. The results show that the proposed methodology can not only represent experts’ evaluation information extensively, but also reveal a logical and consistent sequence related to wind turbine alternatives using reliability information.

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Selection of Wind Turbine Based on Fuzzy Analytic Network Process: A Case Study in China

Cited by 14 publications

References 35 publications

Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

Operation State of the Wind Turbine Pitch System Based on Fuzzy Comprehensive Evaluation

A Novel Z-Fuzzy AHP&EDAS Methodology and Its Application to Wind Turbine Selection

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