Optimistic locating method of transformer voiceprint based on beam and delay algorithm

In view of the on-line inspection mode of transformer voiceprint information during operation, an optimistic location method is proposed to assist the acoustic print diagnosis for fault identification, location and further evaluation work. Firstly, according to the directional location algorithm of antenna array (Beamforming), traditional directional location is extended to general spatial location. Then on the basis of TDOA(Time Difference Of Arrival) algorithm of time domain signal characteristic analysis, integrated weighted processing and data centralization, on the one hand, reduce the edge value, on the other hand, correct the trend of bad data, and improve the existing cross-correlation operation. The simulation results show that the optimized beam-based and weighted TDOA algorithm has excellent performance in 2D localization of transformer voiceprint. These research results can provide a basis for transformer online monitoring and voice print diagnosis.

Denoising and Bad Data Detection in Distribution Phasor Measurements using Filtering, Clustering and Koopman Mode Analysis

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.

Denoising and Bad Data Detection in Distribution Phasor Measurements using Filtering, Clustering and Koopman Mode Analysis

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.