Paper Info
Title
Consideration effect of uncertainty in power system reliability indices using radial basis function network and fuzzy logic theory
Abstract
Reliability assessment of composite power systems is a critical and important part of power investigations especially in the market-driven environments. Therefore, the reliability indices as criteria for the comparison of the reliability of the power systems should be evaluated precisely and carefully. Because of the nonlinear behavior of the systems as the effect of different parameters like weather conditions, load pattern changes and some others, reliability indices always contain much uncertainty. In this paper a neuro-fuzzy based method is proposed to reduce the degree of the uncertainty in the reliability indices and therefore to evaluate the reliability of the composite power systems precisely. Fuzzy logic theory makes it possible to make use of the human experts knowledge in the reliability evaluations. Also by the use of RBFNN and its powerful characteristic to learn any nonlinear mapping between two states it would be possible to evaluate the reliability indices for every short time interval needed so that reliability evaluation in real time would be achievable and feasible. In this paper the RBFNN is trained by the training patterns that are achieved by the use of fuzzy logic theory, then the results are examined on a standard Reliability Test System (RTS-96).
Year
DOI
Venue
2011
10.1016/j.neucom.2011.05.017
Neurocomputing
Keywords
Field
DocType
reliability assessment,reliability index,power investigation,radial basis function network,consideration effect,reliability evaluation,power system,fuzzy logic theory,nonlinear behavior,real time,power system reliability,nonlinear mapping,composite power system,neuro fuzzy,fuzzy logic,radial basis function,artificial neural network,reliability,membership function
Composite power systems,Radial basis function network,Nonlinear system,Fuzzy logic,Electric power system,Artificial intelligence,Membership function,Mathematics,Reliability engineering,Machine learning
Journal
Volume
Issue
ISSN
74
17
0925-2312
Citations 
PageRank 
References 
15
2.58
8
Authors
2
Name
Order
Citations
PageRank
Abdollah Kavousi-Fard126831.99
Haidar Samet2439.68