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. The model is trained using actual-time grid stability indicators like voltage oscillations, frequency excursions, and phase angles and attains a 98.5% accuracy rate with low computational overhead. Comparative analyses prove to be superior in performance compared to traditional deep learning approaches, minimizing false positives and processing time by a considerable margin. The method provides adaptive and robust stability detection, which makes it very well adapted for contemporary smart grids. The system proposed provides an efficient and scalable solution for real-time stability evaluation, enabling the development of smarter and more robust power grids. The model achieved an accuracy of98.5%.