EVALUATION OF GENERATION SYSTEM RELIABILITY INDICES USING RBFNN METHOD
Electrical power system is changing from utility centric to customer centric after introduction of Electricity Act 2003 in India and unbundling of power sector in rest of the world. After unbundling, customer satisfaction has prime importance.Customer expectspower supply 24x7, which can be...
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Yeshwantrao Chavan College of Engineering, India
2021-02-01
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doaj-6c941a3e96c54ab7ab1284d0e589e00f2021-02-20T07:58:22ZengYeshwantrao Chavan College of Engineering, IndiaJournal of Research in Engineering and Applied Sciences2456-64032456-64032021-02-01613237https://doi.org/10.46565/jreas.2021.v06i01.006EVALUATION OF GENERATION SYSTEM RELIABILITY INDICES USING RBFNN METHODRavindra M. Moharil0Prakash S. Kulkarni1Yeshwantrao Chavan College of Engineering, Wanadongri, Hingna Road Nagpur, Maharashtra, India, Pin –441110Department of Electrical Engineering, Vishveshvarya National Institute of Technology, Nagpur, Maharashtra, India. Pin–440011Electrical power system is changing from utility centric to customer centric after introduction of Electricity Act 2003 in India and unbundling of power sector in rest of the world. After unbundling, customer satisfaction has prime importance.Customer expectspower supply 24x7, which can be assuredonly after performingthe reliability analysis of generation system. The purpose of this paper is to present Neural Network (NN) approach, which can overcome limitationsof the conventionalreliability evaluation method such as poor accuracy, complicated models,and large timefor execution. This paper presents anorganized approach forestablishing the learning model with supervision using radial basis function neural network (RBFNN). This model is used for evaluation of various reliabilityindicesused for generation planning. Markov process and basic probabilistic approach is used to develop theinput-output training patterns for neural network. These patterns are normalized and presented to RBFNN. The validation of the proposed technique is confirmed by analyzingRoy Billinton Test System (RBTS) and IEEE-Reliability Test system (IEEE-RTS).http://www.mgijournal.com/Data/Issues_AdminPdf/231/EVALUATION%20OF%20GENERATION%20SYSTEM%20.pdfexpected energy not served (eens)forced outage rate (for)loss of load expectation (lole)orthogonal least square (ols)radial basis function neural network (rbfnn). |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ravindra M. Moharil Prakash S. Kulkarni |
spellingShingle |
Ravindra M. Moharil Prakash S. Kulkarni EVALUATION OF GENERATION SYSTEM RELIABILITY INDICES USING RBFNN METHOD Journal of Research in Engineering and Applied Sciences expected energy not served (eens) forced outage rate (for) loss of load expectation (lole) orthogonal least square (ols) radial basis function neural network (rbfnn). |
author_facet |
Ravindra M. Moharil Prakash S. Kulkarni |
author_sort |
Ravindra M. Moharil |
title |
EVALUATION OF GENERATION SYSTEM RELIABILITY INDICES USING RBFNN METHOD |
title_short |
EVALUATION OF GENERATION SYSTEM RELIABILITY INDICES USING RBFNN METHOD |
title_full |
EVALUATION OF GENERATION SYSTEM RELIABILITY INDICES USING RBFNN METHOD |
title_fullStr |
EVALUATION OF GENERATION SYSTEM RELIABILITY INDICES USING RBFNN METHOD |
title_full_unstemmed |
EVALUATION OF GENERATION SYSTEM RELIABILITY INDICES USING RBFNN METHOD |
title_sort |
evaluation of generation system reliability indices using rbfnn method |
publisher |
Yeshwantrao Chavan College of Engineering, India |
series |
Journal of Research in Engineering and Applied Sciences |
issn |
2456-6403 2456-6403 |
publishDate |
2021-02-01 |
description |
Electrical power system is changing from utility centric to customer centric after introduction of Electricity Act 2003 in India and unbundling of power sector in rest of the world. After unbundling, customer satisfaction has prime importance.Customer expectspower supply 24x7, which can be assuredonly after performingthe reliability analysis of generation system. The purpose of this paper is to present Neural Network (NN) approach, which can overcome limitationsof the conventionalreliability evaluation method such as poor accuracy, complicated models,and large timefor execution. This paper presents anorganized approach forestablishing the learning model with supervision using radial basis function neural network (RBFNN). This model is used for evaluation of various reliabilityindicesused for generation planning. Markov process and basic probabilistic approach is used to develop theinput-output training patterns for neural network. These patterns are normalized and presented to RBFNN. The validation of the proposed technique is confirmed by analyzingRoy Billinton Test System (RBTS) and IEEE-Reliability Test system (IEEE-RTS). |
topic |
expected energy not served (eens) forced outage rate (for) loss of load expectation (lole) orthogonal least square (ols) radial basis function neural network (rbfnn). |
url |
http://www.mgijournal.com/Data/Issues_AdminPdf/231/EVALUATION%20OF%20GENERATION%20SYSTEM%20.pdf |
work_keys_str_mv |
AT ravindrammoharil evaluationofgenerationsystemreliabilityindicesusingrbfnnmethod AT prakashskulkarni evaluationofgenerationsystemreliabilityindicesusingrbfnnmethod |
_version_ |
1724259866516652032 |