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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Smart Grid Stability Prediction Model Using Neural Networks to Handle Missing Inputs.

Madiah Binti Omar1, Rosdiazli Ibrahim2, Rhea Mantri3

  • 1Department of Chemical Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia.

Sensors (Basel, Switzerland)
|June 24, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel neural network model to predict smart grid stability, effectively handling missing data. The enhanced forecasting model improves reliability and efficiency in electrical power systems.

Keywords:
feedforward neural networkforecastfour-node star networkpredictionsmart gridstability

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Smart grids enable bidirectional communication for demand response, requiring stability prediction for reliability.
  • Missing input data due to sensor or system failures is a significant challenge in smart grid operations.
  • Existing research has not addressed the prediction of missing input variables for smart grid stability.

Purpose of the Study:

  • To develop an enhanced forecasting model for predicting smart grid stability.
  • To address the challenge of missing input data in smart grid stability prediction using neural networks.
  • To improve the reliability and efficiency of electrical supply in smart grids.

Main Methods:

  • Utilized neural networks for enhanced forecasting of smart grid stability.
  • Implemented a method to predict missing input data before stability prediction.
  • Employed the Levenberg-Marquardt algorithm for model training with tansig and purelin transfer functions.

Main Results:

  • Four case studies with missing data were successfully conducted.
  • The developed model demonstrated effective prediction of missing data and subsequent stability.
  • Performance was evaluated on a four-node star network using MSE and R2 values.

Conclusions:

  • The enhanced forecasting model shows good performance in predicting smart grid stability.
  • The approach effectively handles missing input data, a novel contribution to the field.
  • The models exhibit excellent training and prediction abilities, enhancing smart grid reliability.