relation: https://khub.utp.edu.my/scholars/16684/
title: Smart Grid Stability Prediction Model Using Neural Networks to Handle Missing Inputs
creator: Omar, M.B.
creator: Ibrahim, R.
creator: Mantri, R.
creator: Chaudhary, J.
creator: Selvaraj, K.R.
creator: Bingi, K.
description: A smart grid is a modern electricity system enabling a bidirectional flow of communication that works on the notion of demand response. The stability prediction of the smart grid becomes necessary to make it more reliable and improve the efficiency and consistency of the electrical supply. Due to sensor or system failures, missing input data can often occur. It is worth noting that there has been no work conducted to predict the missing input variables in the past. Thus, this paper aims to develop an enhanced forecasting model to predict smart grid stability using neural networks to handle the missing data. Four case studies with missing input data are conducted. The missing data is predicted for each case, and then a model is prepared to predict the stability. The Levenberg� Marquardt algorithm is used to train all the models and the transfer functions used are tansig and purelin in the hidden and output layers, respectively. The model�s performance is evaluated on a four-node star network and is measured in terms of the MSE and R2 values. The four stability prediction models demonstrate good performances and depict the best training and prediction ability. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
publisher: MDPI
date: 2022
type: Article
type: PeerReviewed
identifier:   Omar, M.B. and Ibrahim, R. and Mantri, R. and Chaudhary, J. and Selvaraj, K.R. and Bingi, K.  (2022) Smart Grid Stability Prediction Model Using Neural Networks to Handle Missing Inputs.  Sensors, 22 (12).   ISSN 14248220     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131564338&doi=10.3390%2fs22124342&partnerID=40&md5=107e1544d1c00f6cee4323a24b342c72
relation: 10.3390/s22124342
identifier: 10.3390/s22124342