RESEARCH PAPER
Reliability assessment of wind turbine generators by fuzzy universal generating function
| 1 | Center for System Reliability and Safety, School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China Sichuan, 611731, P. R. China |
Publication date: 2021-06-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(2):308–314
HIGHLIGHTS
- The fuzzy states of the DFIG systems are provided.
- All components’ states are given as triangular fuzzy number based on experts’ experience.
- The reliability assessment of the DFIG based on the FUGF is performed.
KEYWORDS
reliability assessmentdouble fed induction generatormulti-state systemfuzzy universalgenerating function
ABSTRACT
Wind power has been widely used in the past decade because of its safety and cleanness.
Double fed induction generator (DFIG), as one of the most popular wind turbine generators,
suffers from degradation. Therefore, reliability assessment for this type of generator is of
great significance. The DFIG can be characterized as a multi-state system (MSS) whose
components have more than two states. However, due to the limited data and/or vague judgments from experts, it is difficult to obtain the accurate values of the states and thus it inevitably contains epistemic uncertainty. In this paper, the fuzzy universal generating function
(FUGF) method is utilized to conduct the reliability assessment of the DFIG by describing
the states using fuzzy numbers. First, the fuzzy states of the DFIG system’s components
are defined and the entire system state is calculated based the system structure function.
Second, all components’ states are determined as triangular fuzzy numbers (TFN) according
to experts’ experiences. Finally, the reliability assessment of the DFIG based on the FUGF
is conducted.
REFERENCES (34)
1.
Carroll J, McDonald A, Mcmillan D. Reliability comparison of wind turbines with DFIG and PMG drive trains. In 2015 IEEE Power & Energy Society General Meeting, Denver, CO: IEEE 2015; 663-670, https://doi.org/10.1109/PESGM.....
2.
Chen JK, He Y, Wei W. Reliability analysis and optimization of equal load-sharing K-out-of-N phased-mission systems. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2015; 17(2): 250-259, https://doi.org/10.17531/ein.2....
3.
China National Energy Administration. National electric power industry statistics in 2019. 2020.
4.
China National Energy Administration. National electric power industry statistics in 2018. 2019.
5.
Cui XY, Li TY, Wang S P, Shi J, Ma Z H. Reliability modeling based on power transfer efficiency and its application to aircraft actuation system. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(2): 282-296, https://doi.org/10.17531/ein.2....
6.
Ding Y, Lisnianski A. Fuzzy universal generating functions for multi-state system reliability assessment. Fuzzy Sets and System 2007; 159: 307-324, https://doi.org/10.1016/j.fss.....
7.
Dong W, Liu S, Zhang Q, Mierzwiak R, Fang Z, Cao Y. Reliability assessment for uncertain multi-state systems: an extension of fuzzy universal generating function. International Journal of Fuzzy Systems 2019; 21, 945-953, https://doi.org/10.1007/s40815....
8.
Eryilmaz S. Reliability analysis of multi-state system with three-state components and its application to wind energy. Reliability Engineering & System Safety 2017; 172: 58-63, https://doi.org/10.1016/j.ress....
9.
Gao H, Zhang X. A novel reliability analysis method for fuzzy multi-state systems considering correlation. IEEE Access 2019; 7: 153194-153204, https://doi.org/10.1109/ACCESS....
10.
Gao P, Xie L, Hu W, Liu C, Feng J. Dynamic fuzzy reliability analysis of multistate systems based on universal generating function. Mathematical Problems in Engineering 2018; 2018: 1-8, https://doi.org/10.1155/2018/6....
11.
Huang HZ. Reliability analysis method in the presence of fuzziness attached to operation time. Microelectronics Reliability 1995; 35: 1483-1487, https://doi.org/10.1016/0026-2....
12.
Huang HZ, Tong X, Zuo MJ. Posbist fault tree analysis of coherent systems. Reliability Engineering & System Safety 2004; 84: 141-148, https://doi.org/10.1016/j.ress....
13.
Huang HZ, Zuo MJ, Sun Z. Bayesian reliability analysis for fuzzy lifetime data. Fuzzy Sets and System 2006; 157: 1674-86, https://doi.org/10.1016/j.fss.....
14.
Jaiswal N, Negi S, Singh S. Reliability analysis of non-repairable weighted k-out-of-n system using belief universal generating function. International Journal of Industrial and Systems Engineering 2018; 28: 300-318, https://doi.org/10.1504/IJISE.....
15.
Khaniyev T, Baskir M B, Gokpinar F, Mirzayev F. Statistical distribution and reliability functions with type-2 fuzzy parameters. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(2): 268-274, https://doi.org/10.17531/ein.2....
16.
Levitin G. The universal generating function in reliability analysis and optimization. London: Springer, 2005.
17.
Li Y, Ding Y, Zio E. Random fuzzy extension of the universal generating function approach for the reliability assessment of multi-state systems under aleatory and epistemic uncertainties. IEEE Transactions on Reliability 2014; 63: 13-25, https://doi.org/10.1109/TR.201....
18.
Li YF, Huang HZ, Mi J, Peng W, Han X. Reliability analysis of multi-state systems with common cause failures based on Bayesian network and fuzzy probability. Annals of Operations Research 2019; https://doi.org/10.1007/s10479....
19.
Li YF, Liu Y, Huang T, Huang HZ, Mi J. Reliability assessment for systems suffering common cause failure based on Bayesian networks and proportional hazards model. Quality and Reliability Engineering International 2020; 36(7): 2509-2520, https://doi.org/10.1002/qre.27....
20.
Lisnianski A, Frenkel I, Ding Y. Multi-State System Reliability Analysis and Optimization for Engineers and Industrial Managers. Spring Verlag: Berlin, New York, 2010, https://doi.org/10.1007/978-1-....
21.
Liu Y, Huang HZ. Reliability assessment for fuzzy multi-state systems. International Journal of Systems Science 2009; 41: 365-379, https://doi.org/10.1080/002077....
22.
Mi J, Li YF, Liu Y, Yang YJ, Huang HZ. Belief universal generating function analysis of multi-state systems under epistemic uncertainty and common cause failures. IEEE Transactions on Reliability 2015; 64(4): 1300-1309, https://doi.org/10.1109/TR.201....
23.
Mi J, Li YF, Peng W, Huang HZ. Reliability analysis of complex multi-state system with common cause failure based on evidential networks. Reliability Engineering & System Safety 2018; 174: 71-81, https://doi.org/10.1016/j.ress....
24.
Mlynarski S, Pilch R, Smolnik M, Szybka J, Wiazania G. A method for rapid evaluation of k-out-of-n systems reliability. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(1): 170-176, https://doi.org/10.17531/ein.2....
25.
Negi S, Singh S. Fuzzy reliability evaluation of linear m-consecutive weighted-k-out-of-r-from-n: F systems. International Journal of Computing Science and Mathematics 2019; 10: 606-621, https://doi.org/10.1504/IJCSM.....
26.
Qin JL, Niu YG, Li Z. A combined method for reliability analysis of multiple-state system of minor-repairable components. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2016; 18(1): 80-88, https://doi.org/10.17531/ein.2....
27.
Wu H. Fuzzy reliability estimation using Bayesian approach. Computers and Industrial Engineering 2004; 46: 467-93, https://doi.org/10.1016/j.cie.....
28.
Xiahou T, Liu Y. Reliability bounds for multi-state systems by fusing multiple sources of imprecise information. IISE Transactions 2020; 52(9): 1014-1031, https://doi.org/10.1080/247258....
29.
Xiahou T, Zeng Z, Liu Y. Remaining useful life prediction by fusing experts' knowledge and condition monitoring information. IEEE Transactions on Industrial Informatics 2020, https://doi.org/10.1109/TII.20....
30.
Xiao H, Cao M. Joint Optimization of redundancy and preventive maintenance of a power grid system considering economic cost. Energy 2020; 201: 118470, https://doi.org/10.1016/j.ener....
31.
Xiao H, Cao MH, Kou G, Yuan XJ. Optimal element allocation and sequencing of multi-state series systems with two levels of performance sharing. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 2019, https://doi.org/10.1177/174800....
32.
Xiao H, Yi KX, Kou G, Xing LD. Reliability of a two‐dimensional demand‐based networked system with multistate components. Naval Research Logistics (NRL), 2020, 67(6): 453-468, https://doi.org/10.1002/nav.21....
33.
Xiao H, Zhang YY, Xiang YS, Peng R. Optimal design of a linear sliding window system with consideration of performance sharing. Reliability Engineering & System Safety, 2020, 198: 106900, https://doi.org/10.1016/j.ress....
34.
Zhou D, Blaabjerg F, Lau M. Cost on reliability and production loss for power converters in the doubly fed induction generator to support modern grid codes. Electric Power Components and Systems 2016; 44: 152-164, https://doi.org/10.1080/153250....
CITATIONS (10):
1.
Fatigue life prediction of wire rope based on grey particle filter method under small sample condition
Dan Zhao, Yu-Xin Liu, Xun-Tao Ren, Jing-Zi Gao, Shao-Gang Liu, Li-Qiang Dong, Ming-Shen Cheng
Eksploatacja i Niezawodnosc - Maintenance and Reliability
Dan Zhao, Yu-Xin Liu, Xun-Tao Ren, Jing-Zi Gao, Shao-Gang Liu, Li-Qiang Dong, Ming-Shen Cheng
Eksploatacja i Niezawodnosc - Maintenance and Reliability
2.
Probabilistic fatigue life prediction of an aero-engine turbine shaft
Jun Wu, Hong-Zhong Huang, Yan-Feng Li, Song Bai, Ao-Di Yu
Aircraft Engineering and Aerospace Technology
Jun Wu, Hong-Zhong Huang, Yan-Feng Li, Song Bai, Ao-Di Yu
Aircraft Engineering and Aerospace Technology
3.
Application of Fuzzy Universal Generating Function in Reliability Assessment of Thermal Power Plants
Aayushi Chachra, Akshay Kumar, Mangey Ram
2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)
Aayushi Chachra, Akshay Kumar, Mangey Ram
2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)
4.
Innovative application of quality methods in the homogeneity assessment of the F-16 aircraft group in terms of generated noise
Agnieszka Misztal, Grzegorz Szymanski, Wojciech Misztal, Pawel Komorski
Eksploatacja i Niezawodnosc - Maintenance and Reliability
Agnieszka Misztal, Grzegorz Szymanski, Wojciech Misztal, Pawel Komorski
Eksploatacja i Niezawodnosc - Maintenance and Reliability
5.
A new method for analysis of Multi-State systems based on Multi-valued decision diagram under epistemic uncertainty
Elena Zaitseva, Vitaly Levashenko, Jan Rabcan
Reliability Engineering & System Safety
Elena Zaitseva, Vitaly Levashenko, Jan Rabcan
Reliability Engineering & System Safety
6.
Fatigue Life of Compacted Wire Ropes for Applications in Deep Mining
Bogusław Kubiś, Grzegorz Olszyna, Piotr Szade, Andrzej Tytko
Management Systems in Production Engineering
Bogusław Kubiś, Grzegorz Olszyna, Piotr Szade, Andrzej Tytko
Management Systems in Production Engineering
7.
An evidential network approach to reliability assessment by aggregating system‐level imprecise knowledge
Tudi Huang, Zhidong Shao, Tangfan Xiahou, Yu Liu
Quality and Reliability Engineering International
Tudi Huang, Zhidong Shao, Tangfan Xiahou, Yu Liu
Quality and Reliability Engineering International
8.
Some Stochastic Aspects of Safety Work of Steel Wire Ropes Used in Mining-Shaft Hoists
Andrzej Tytko, Grzegorz Olszyna, Grzegorz Kocór, Mariusz Szot
Sustainability
Andrzej Tytko, Grzegorz Olszyna, Grzegorz Kocór, Mariusz Szot
Sustainability
9.
Intuitionistic fuzzy approach to reliability assessment of multi-state systems
Aayushi Chachra, Akshay Kumar, Mangey Ram
Mathematics and Computers in Simulation
Aayushi Chachra, Akshay Kumar, Mangey Ram
Mathematics and Computers in Simulation
10.
Fault State Prediction Model of Repaired Equipment Considering Maintenance Effect
Jiahui Wang, Lin Ma, Ankang Chen, Qiannan Liu, Mingshun Ma
2023 IEEE International Conference on Prognostics and Health Management (ICPHM)
Jiahui Wang, Lin Ma, Ankang Chen, Qiannan Liu, Mingshun Ma
2023 IEEE International Conference on Prognostics and Health Management (ICPHM)
Share
RELATED ARTICLE
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.