Stochastic behaviour analysis of power generating unit in thermal power plant using fuzzy methodology

被引：14

作者：

Panchal D. ^{[1
]}

Kumar D. ^{[1
]}

机构：

[1] Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand

来源：

OPSEARCH | 2016年 / 53卷 / 1期

关键词：

Failure mode effect analysis (FMEA); Fuzzy decision support system (FDSS); Fuzzy methodology (FM); Petri net (PN); Root cause analysis (RCA); Thermal power plant;

D O I：

10.1007/s12597-015-0219-4

中图分类号：

学科分类号：

摘要：

The aim of this research is to study the stochastic behaviour of a PGU (power generating unit) of a medium size coal fired thermal power plant using fuzzy methodology (FM). The PN approach was used to depict series and parallel configurations of the subsystems constituting the PGU. To study the failure dynamics of the considered system, various reliability parameters such as failure rate, repair time, MTBF, ENOF, availability and reliability of the system were computed using fuzzy λ-τ approach. RCA was conducted to determine the failure causes of various subsystems of the PGU. FMEA was carried out to determine the RPN scores of the various failure causes of different components. On the basis of these RPN scores, critical components were identified and ranked on the basis of criticality. Further, the limitations of the traditional FMEA approach were overcome by introducing a Fuzzy decision support system (FDSS). Findings of this study would be communicated to field engineers/system analysts of the considered system to help them understand and anticipate system behavior, and implement appropriate and effective maintenance policies for improving system availability. © 2015, Operational Research Society of India.

引用

页码：16 / 40

页数：24

共 46 条

[1]

Aksu S., Aksu S., Osman T., Reliability and availability of pod propulsion systems, Qual. Reliab. Eng. Int., 22, 4, pp. 41-58, (2006)

[2]

Cai K.Y., System failure engineering and fuzzy methodology: an introductory overview, Fuzzy Sets Syst., 83, pp. 113-133, (1996)

[3]

Modarres M., Kaminsky M.P., Reliability Engineering and Risk Analysis, (1999)

[4]

Kumar S., Kumar D., Mehta N.P., Steady state behaviour and maintenance planning of a desulphurization system in a urea fertilizer plant, Microelectron. Reliab., 37, 6, pp. 949-953, (1996)

[5]

Arora N., Kumar D., Stochastic analysis and maintenance planning of ash handling system in thermal power plant, Microelectron. Reliab., 37, 5, pp. 819-834, (2000)

[6]

Arora N., Kumar D., Availability analysis of steam and power generation systems in thermal power plant, Microelectron. Reliab., 37, pp. 795-799, (1997)

[7]

Sii H.S., Ruxton S.H., Wang J.T., A fuzzy logic based approach to qualitative safety modeling for marine systems, Reliab. Eng. Syst. Saf., 73, pp. 19-34, (2001)

[8]

Sergaki A., Kalaitzakis K., A fuzzy knowledge based method for maintenance planning in a power system, Reliab. Eng. Syst. Saf., 77, pp. 19-30, (2002)

[9]

Soroudi A., Possibilistic-scenario model for DG impact assessment on distribution networks in an uncertain environment, IEEE Trans. Power Syst., 27, 3, pp. 1283-1293, (2012)

[10]

Soroudi A., Ehsan E., IGDT based robust decision making tool for DNOs in load procurement under severe uncertainty, IEEE Trans. Smart Grid, 4, 2, pp. 886-895, (2012)

← 1 2 3 4 5 →