Denoising and Detection of Bad Data 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 preprocess 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 smoothing wavelet denoising method is used to remove high-frequency noises. An ensemble approach built upon the margin-based maximum likelihood estimator (MB-MLE) method is developed to detect anomalies in denoised data by integration of the results from different base detectors including Hampel filter, Quartile detector, and DBSCAN. 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 using Kalman filter (KF). The parameters of the KF are adjusted adaptively based on similarity to the training dataset 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 simulator.