Power System Neural Network
Deep learning model using PyTorch to predict branch overloads in electric power grids, implementing neural networks for critical infrastructure monitoring.
Overview
A deep learning project using neural networks to predict branch overloads in electric power systems. The project implements a multi-layer neural network in PyTorch to analyze power flow data and predict potential grid failures, critical for maintaining power grid stability and preventing cascading blackouts.
Technologies
Key Features
- ✓Custom neural network architecture design
- ✓Multi-layer fully connected network with ReLU activation
- ✓Feature scaling and normalization pipeline
- ✓Binary classification for overload prediction
- ✓Model training with validation monitoring
- ✓Performance evaluation (accuracy, precision, recall)
Challenges
Designing network architecture for power system data characteristics, handling class imbalance in overload events, selecting optimal hyperparameters, and ensuring model generalizes across different grid configurations.
Results
Successfully developed a neural network model for power system monitoring, demonstrating application of deep learning to critical infrastructure problems.