UGC Approved Journal no 63975

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 5 Issue 7
July-2018
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIRC006349


Registration ID:
184695

Page Number

227-235

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Title

Power system security monitoring through classification approach using Multilayer Feed Forward Neural Network

Abstract

The power system is a complex network with numerous equipment’s interconnected which are forced to operate under highly stressed conditions closer to their limits. One of the major aspect for the secure operation of the system can be achieved through security assessment, context to which the power system static security assessment is necessary to evaluate the security status under contingency scenario. The conventional method of security assessment involves solving the set of nonlinear load flow equations. But the complexity and computation time makes them infeasible for real time security assessment of large power system networks. This necessitates the need for an efficient approach to assess the security status in short period of time. Thus, it is necessary to design an effective security assessment model. Multi-layer feed forward artificial neural network (MLFFN) is proposed to implement the classification approach for power system static security assessment. The contingency classification, is done based on the composite security index which is capable of accurately differentiating the secure and non-secure cases. For each contingency case as well as for base case condition, the composite security index is computed using the full Newton Raphson load flow analysis. The proposed artificial neural network (ANN) model take features selected using single ranking method and the probable contingencies as the input, assessing the system security by classifying the credible contingencies as secure and insecure.

Key Words

Feature selection, classification, power system security, Neural Network.

Cite This Article

"Power system security monitoring through classification approach using Multilayer Feed Forward Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.227-235, July-2018, Available :http://www.jetir.org/papers/JETIRC006349.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Power system security monitoring through classification approach using Multilayer Feed Forward Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp227-235, July-2018, Available at : http://www.jetir.org/papers/JETIRC006349.pdf

Publication Details

Published Paper ID: JETIRC006349
Registration ID: 184695
Published In: Volume 5 | Issue 7 | Year July-2018
DOI (Digital Object Identifier):
Page No: 227-235
Country: -, -, - .
Area: Engineering
ISSN Number: 2349-5162


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