Wavelet-ARIMA-based Forensic Analysis of Synchrophasor Data Using Machine Learning

— Integration of distributed energy resources into
power grids fosters the development of precise monitoring,
protection, and control applications by employing immense
spatiotemporal data from micro-phasor measurement units
(µPMUs). For enhanced situational awareness, a comprehensive
methodology is required for real-time synchro phasor forensic
analysis, using advanced machine learning techniques to detect
and classify anomalies in grid events. This paper presents a twostage analytical framework that combines WaveletAutoregressive Integrated Moving Average (ARIMA)-based
analysis with a machine learning approach to enhance the
identification and classification of events by leveraging
historical frequency and spectrum data. The raw data from the
New England ISO and the European Continental Split dataset
is preprocessed in the initial phase as it includes multiple events.
The process involves Stationary Wavelet Transform (SWT) for
denoising and sliding window ARIMA model to identify the
Rolling Standard Deviation (RSD) for feature extraction and
threshold setting. The frequency excursions and oscillations are
classified based on the Synchro phasor Event Detection
Algorithm (SPEDA) as per statistical thresholds. The retrieved
features of the detected and localized events are cross-validated
using machine learning classifiers in the next stage, enhancing
the overall efficiency and effectiveness of the study. The study
will demonstrate that advanced computing facilities accelerate
sophisticated calculations and reduce model training time.