A data analytics and machine learning approach for online electrical event detection from wide área measurement systems

The modernization of electrical systems requires new tools capable of handling large volumes of real-time data. In this book, Dr. Rafael Rodrigues presents an original scientific contribution by proposing the PCA-Z method, an approach based on Principal Component Analysis (PCA) and Z-score for the automatic detection of electrical events using Synchrophasor Measurement Systems (PMUs). Applied to real-world data from Companhia Paranaense de Energia (Copel) and validated against events from the Brazilian National Interconnected System (SIN), the method demonstrates superior computational performance and high robustness when compared to established techniques in the literature, such as Isolation Forest, K-Means, and LoOP. The work also integrates Artificial Neural Networks for the identification and classification of events, such as faults, oscillations, and instabilities, reinforcing the role of Artificial Intelligence in real-time system operation. More than an academic contribution, this book presents a practical advancement for control centers, post-operation teams, and system reliability specialists, offering both technical foundations and real-world applications for the evolution of smart grids.
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