Denoising and Bad Data Detection in Distribution Phasor Measurements
using Filtering, Clustering and Koopman Mode Analysis
Abstract
Distribution-level phasor measurement units (D-PMU) data are prone to
different types of anomalies given complex data flow and processing
infrastructure in an active power distribution system with enhanced
digital automation. It is essential to pre-process the data before being
used by critical applications for situational awareness and control. In
this work, two approaches for detection of data anomalies are introduced
for offline (larger data processing window) and online (shorter data
processing window) applications. A margin-based maximum likelihood
estimator (MB-MLE) method is developed to detect anomalies by
integrating the results of different base detectors including Hampel
filter, Quartile detector and DBSCAN. A smoothing wavelet denoising
method is used to remove high-frequency noises. The processed data with
offline analysis is used to fit a model to the underlying dynamics of
synchrophasor data using Koopman Mode Analysis, which is subsequently
employed for online denoising and bad data detection (BDD) using Kalman
Filter (KF). The parameters of the KF are adjusted adaptively based on
similarity to the training data set for model fitting purposes.
Developed techniques have been validated for the modified IEEE test
system with multiple D-PMUs, modeled and simulated in real-time for
different case scenarios using the OPAL-RT Hardware-In-the-Loop (HIL)
Simulator.