Neural Networks for Real-Time Power Grid Stability Analysis in EEE Systems

Abstract

Power grid stability requirements in real-time need to be handled to achieve reliable operations for electrical and electronic engineering (EEE) systems. Researchers investigated a power grid stability analysis improvement method through the combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) neural networks (CNN + LSTM). The applied implementation of TensorFlow and Keras operates on real-time grid sensor data and combines voltage fluctuations and load variations for anticipating potential instabilities and faults. Anomalies become easier to detect because the proposed system obtains an accurate prediction rate which performs more efficiently than standard processes. Real-time adjustments of the model create an improved combination of fault prevention capability with rapid system responses and effective load distribution performances. The research delivers an AI-based method for predicting grid stability which helps develop secure and intelligent smart grid platforms.