A Novel Framework for Synchrophasor Based Online Recognition and Efficient Post-Mortem Analysis of Disturbances in Power Systems

Abstract

Synchrophasor based applications become more and more popular in today’s control centers to monitor and control transient system events. This can ensure secure system operation when dealing with bidirectional power flows, diminishing reserves and an increased number of active grid components. Today’s synchrophasor applications provide a lot of additional information about the dynamic system behavior but without significant improvement of the system operation due to the lack of interpretable and condensed results as well as missing integration into existing decision-making processes. This study presents a holistic framework for novel machine learning based applications analyzing both historical as well as online synchrophasor data streams. Different methods from dimension reduction, anomaly detection as well as time series classification are used to automatically detect disturbances combined with a web-based online visualization tool. This enables automated decision-making processes in control centers to mitigate critical system states and to ensure secure system operations (e.g., by activating curate actions). Measurement and simulation-based results are presented to evaluate the proposed synchrophasor application modules for different use cases at the transmission and distribution level.

Feasibility Analysis of Online Uncertainty Metrics for PMU-Based State Estimation

“…Once characterized the relationship between nRMSE and TVE is in standard test conditions, we can consider a realistic data set. In this way, we investigate whether the nRMSE can be used to detect anomalous or contradictory measurement results, e.g., due to contingencies or due to PMU malfunctions [37].…”

Combined Network Intrusion and Phasor Data Anomaly Detection for Secure Dynamic Control Centers

“…The GRU-AE and GRU-FC models are implemented as Seq2Seq-models with additional attention mechanisms to learn the normal system behavior based on the latent representations f AE and f FC for various contingencies and transient system states using uncorrupted PMU signals. The GRU encoder computes the hidden state vectors from the input signals and passes them to the attention model to extract the latent representationsas introduced in [3]. The GRU decoder uses these latent representations as well as the last hidden state of the encoder to compute the reconstructions or forecasts.…”

“…As introduced in [1][2][3][4], dynamic control centers extend traditional SCADA-based system architectures with new assistant functionalities (e.g., DSA, WAMS) to increase the situational awareness and to improve the grid operation to handle critical grid situations or disturbed system states. This requires the integration of additional monitoring and control components (e.g., PMUs, HVDC, FACTS), and an increased utilization of TCP/IPbased transmission protocols as well as automation processes.…”

Siamese recurrent neural networks for the robust classification of grid disturbances in transmission power systems considering unknown events

“…The automated processing and analysis of phasor measurement units (PMU) in transmission and distribution power systems has been a main research field for several years [1][2][3][4]. PMUs provide time-synchronised frequency, voltage and current phasor measurements with a high time resolution and enable new dynamic control centre functions for the monitoring and control of future power systems.…”