Intelligent Stability Detection in Smart Power Grids using Optimized Deep Learning Models

The swift integration of distributed generation
and renewable energy in smart power grids requires
sophisticated stability detection mechanisms to avoid
failures and guarantee reliable performance. The article
introduces an optimized intelligent stability detection
model based on deep learning, consisting of
Convolutional Neural Networks (CNNs), Bidirectional
Long Short-Term Memory (BiLSTM) networks, and an
attention mechanism for precise real-time monitoring.
The optimizer dynamically adapts hyperparameters using
a reinforcement learning-based optimizer for improved
efficiency and performance.