Latent Vector Perturbation Recognition for Power Grid Root Cause Analysis

The system performs preliminary actions by pre-training a machine learning data model offline using comprehensive perturbation data arrays. This pre-trained model captures complex perturbation patterns and relationships in advance, enabling rapid online inference without time-consuming real-time simulations. The offline training phase prepares the model to quickly identify root causes during actual operations.

The patent replaces traditional mechanical simulation systems with a machine learning-based data model. Instead of running complex physics-based simulations online, the system uses a trained neural network model that has learned perturbation patterns offline. This substitution transforms the analysis from computationally intensive mechanical simulations to efficient statistical inference, dramatically improving analysis speed while maintaining accuracy.

The system implements self-service by enabling automatic root-cause identification through the machine learning model. The model independently analyzes perturbation data, compares it against trained patterns, and automatically identifies root causes without requiring operator intervention. This automation handles routine analysis tasks, freeing operators to focus on complex decision-making while maintaining consistent, reproducible results.

The system incorporates feedback mechanisms where the machine learning model continuously learns from new perturbation data and operator corrections. The model receives feedback from actual system responses and operator validations, refining its understanding of perturbation patterns over time. This feedback loop enhances the model's adaptability while maintaining automation, allowing it to handle increasingly complex situations.