A Bayesian Framework for Uncertainty-Aware Explanations in Power Quality Disturbance Classification

The project is a specialized academic contribution at the intersection of Bayesian statistics, XAI, and power systems engineering. With 0 stars and 3 forks at 2 days old, it is currently a reference implementation for a research paper rather than a production-grade tool. Its defensibility is low (3/10) because the methodology, while intellectually rigorous, can be replicated by any researcher familiar with Bayesian Neural Networks and signal processing. However, its 'frontier risk' is also low because general-purpose AI labs (OpenAI, Anthropic) are unlikely to focus on the niche telemetry of power grids. The primary value lies in its domain-specific application—interpreting disturbances like voltage sags, swells, and harmonics with a measure of confidence, which is critical for utility providers. Competitors include generic XAI frameworks like Captum or SHAP, but those lack the uncertainty-awareness this paper proposes. The project's longevity depends on whether these Bayesian techniques are integrated into broader Industrial IoT or Smart Grid monitoring platforms.