Reliability assessment of wind turbine generators by fuzzy universal generating function

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. Highlights Abstract

[1]  Gregory Levitin,et al.  The Universal Generating Function in Reliability Analysis and Optimization , 2005 .

[2]  Yi Ding,et al.  Fuzzy universal generating functions for multi-state system reliability assessment , 2008, Fuzzy Sets Syst..

[3]  Serkan Eryilmaz.  Reliability analysis of multi-state system with three-state components and its application to wind energy , 2018, Reliab. Eng. Syst. Saf..

[4]  Yi Ding,et al.  Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers , 2010 .

[5]  Shaoping Wang,et al.  Reliability modeling based on power transfer efficiency and its application to aircraft actuation system , 2020 .

[6]  Enrico Zio,et al.  Random Fuzzy Extension of the Universal Generating Function Approach for the Reliability Assessment of Multi-State Systems Under Aleatory and Epistemic Uncertainties , 2014, IEEE Transactions on Reliability.

[7]  Hui Xiao,et al.  Reliability of a two‐dimensional demand‐based networked system with multistate components , 2020, Naval Research Logistics (NRL).

[8]  Robert Pilch,et al.  A Method for rapid evaluation of k-out-of-n systems reliability , 2018 .

[9]  He Yan,et al.  Reliability analysis and optimization of equal load-sharing k-out-of-n phased-mission systems , 2015 .

[10]  Yu Liu,et al.  Reliability bounds for multi-state systems by fusing multiple sources of imprecise information , 2020, IISE Trans..

[11]  Weiwen Peng,et al.  Reliability analysis of complex multi-state system with common cause failure based on evidential networks , 2018, Reliab. Eng. Syst. Saf..

[12]  Hsien-Chung Wu,et al.  Fuzzy reliability estimation using Bayesian approach , 2004, Comput. Ind. Eng..

[13]  M. B. Baskir,et al.  Statistical distributions and reliability functions with type-2 fuzzy parameters , 2019, Ekspolatacja i Niezawodnosc - Maintenance and Reliability.

[14]  Rui Peng,et al.  Optimal design of a linear sliding window system with consideration of performance sharing , 2020, Reliab. Eng. Syst. Saf..

[15]  Yu Liu,et al.  Remaining Useful Life Prediction by Fusing Expert Knowledge and Condition Monitoring Information , 2021, IEEE Transactions on Industrial Informatics.

[16]  Qin Zhang,et al.  Reliability Assessment for Uncertain Multi-state Systems: An Extension of Fuzzy Universal Generating Function , 2019, Int. J. Fuzzy Syst..

[17]  D. Angersbach,et al.  Action spectra for light-controlled pupal pigmentation in Pieris brassicae: melanization and level of bile pigment. , 1974, Journal of insect physiology.

[18]  Hui Xiao,et al.  Balancing the demand and supply of a power grid system via reliability modeling and maintenance optimization , 2020 .

[19]  Xiaojun Yuan,et al.  Optimal element allocation and sequencing of multi-state series systems with two levels of performance sharing: , 2020 .

[20]  Hong-Zhong Huang,et al.  Posbist fault tree analysis of coherent systems , 2004, Reliab. Eng. Syst. Saf..

[21]  Hong-Zhong Huang,et al.  Bayesian reliability analysis for fuzzy lifetime data , 2006, Fuzzy Sets Syst..

[22]  Weiwen Peng,et al.  Reliability analysis of multi-state systems with common cause failures based on Bayesian network and fuzzy probability , 2019, Ann. Oper. Res..

[23]  Xiaoqiang Zhang,et al.  A Novel Reliability Analysis Method for Fuzzy Multi-State Systems Considering Correlation , 2019, IEEE Access.

[24]  Frede Blaabjerg,et al.  Cost on Reliability and Production Loss for Power Converters in the Doubly Fed Induction Generator to Support Modern Grid Codes , 2016 .

[25]  Hong-Zhong Huang,et al.  Reliability assessment for systems suffering common cause failure based on Bayesian networks and proportional hazards model , 2020, Qual. Reliab. Eng. Int..

[26]  Seema Negi,et al.  Fuzzy reliability evaluation of linear m-consecutive weighted-k-out-of-r-from-n: F systems , 2019, Int. J. Comput. Sci. Math..

[27]  Seema Negi,et al.  Reliability analysis of non-repairable weighted k-out-of-n system using belief universal generating function , 2018 .

[28]  Hong-Zhong Huang,et al.  Reliability analysis method in the presence of fuzziness attached to operating time , 1995 .

[29]  Zheng Li,et al.  A combined method for reliability analysis of multi-state system of minor-repairable components , 2016 .

[30]  Hong-Zhong Huang,et al.  Belief Universal Generating Function Analysis of Multi-State Systems Under Epistemic Uncertainty and Common Cause Failures , 2015, IEEE Transactions on Reliability.

[31]  Hong-Zhong Huang,et al.  Reliability assessment for fuzzy multi-state systems , 2010, Int. J. Syst. Sci..

[32]  Chang Liu,et al.  Dynamic Fuzzy Reliability Analysis of Multistate Systems Based on Universal Generating Function , 2018 .