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

7.95 impact factor calculated by Google scholar

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Published Paper ID:
JETIR180Z016


Registration ID:
187200

Page Number

825-829

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Title

A Hopfield Neural Networks in Identification of Redistribution Energy based Load Balancing

Abstract

These works involve the purpose of multilayer Hop field neural network and dynamic neural network for the load frequency control of two-area power systems. Energy efficient wireless networks are the primary research study objective for billions of device applications like IoT (Internet of Things), smart grids and CPS. Monitoring of multiple physical events using sensors and data collection at central gateways is the all-purpose architecture follow by most commercial, residential and test bed implementations. The majority of the proceedings monitored at usual intervals are mainly redundant/minor variations leading to large wastage of data storage resources in big data servers and communication energy at relay and sensor nodes. In this project a novel architecture of Hop field Neural Network (NN) based day ahead steady state forecasting engine is implementing at the gateway using historical database. Gateway generate an optimal transmit schedules based on NN outputs thereby reducing the redundant sensor data when there is minor variations in the respective predicted sensor estimates. It is observed that NN based load forecasting for power monitoring system predicts load with less than 3% Mean Absolute Percentage Error (MAPE). Gateway forward transmit schedules to all power sensing nodes day ahead to reduce sensor and relay nodes communication energy. Matlab based simulation for evaluating the benefits of proposed model for extending the wireless network life time is developed and confirmed with an emulation scenario of our testbed. Network life time is improved by 43% from the observed results using proposed model.This paper is mainly concerned with an investigation of the suitability of Hopfield neural network structures in solving the power economic dispatch problem.

Key Words

Control, Transient Analysis, Load Flow, Hopfield Model, Recall and Recognition

Cite This Article

"A Hopfield Neural Networks in Identification of Redistribution Energy based Load Balancing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.825-829, July-2018, Available :http://www.jetir.org/papers/JETIR180Z016.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

"A Hopfield Neural Networks in Identification of Redistribution Energy based Load Balancing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp825-829, July-2018, Available at : http://www.jetir.org/papers/JETIR180Z016.pdf

Publication Details

Published Paper ID: JETIR180Z016
Registration ID: 187200
Published In: Volume 5 | Issue 7 | Year July-2018
DOI (Digital Object Identifier):
Page No: 825-829
Country: -, -, -- .
Area: Engineering
ISSN Number: 2349-5162


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