P

Principal Component Analysis

This thesis addresses the challenges of modernizing Germany's power grid, quantifying risks posed by renewable energy in terms of grid stability. Stability measures are defined. Variability is quantified by fitting Gaussian Mixture Models (GMM) Principal on the Component Analysis (PCA) which is used for dimensionality reduction. Using MCMC techniques find low-probability patterns linked to blackouts and desynchronization.

Python libraries for Principal Component Analysis-based (PCA) model order reduction, clustering and data analysis.

Perform PCA followed by k-means and hierarchical clustering analysis on the Breast Cancer Wisconsin data set