TY  - JOUR
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131564338&doi=10.3390%2fs22124342&partnerID=40&md5=107e1544d1c00f6cee4323a24b342c72
A1  - Omar, M.B.
A1  - Ibrahim, R.
A1  - Mantri, R.
A1  - Chaudhary, J.
A1  - Selvaraj, K.R.
A1  - Bingi, K.
JF  - Sensors
VL  - 22
Y1  - 2022///
SN  - 14248220
PB  - MDPI
N1  - cited By 5
N2  - 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 Levenbergâ?? 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 modelâ??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.
IS  - 12
KW  - Electric power transmission networks; Feedforward neural networks; Input output programs; Smart power grids; Stability; Stars; Systems engineering
KW  -  Four-node star network; Grid stability; Input datas; Missing data; Missing inputs; Neural-networks; Prediction modelling; Smart grid; Stability prediction; STAR network
KW  -  Forecasting
KW  -  algorithm; computer system
KW  -  Algorithms; Computer Systems; Neural Networks
KW  -  Computer
ID  - scholars16684
TI  - Smart Grid Stability Prediction Model Using Neural Networks to Handle Missing Inputs
AV  - none
ER  -