Adaptive Hoeffding Tree with Transfer Learning for Streaming Synchrophasor Data Sets
This work addresses the need for faster real-time anomaly detection in synchrophasor data for power grid monitoring, though it appears incremental as it builds on existing Hoeffding tree and transfer learning techniques.
The paper tackled the problem of detecting anomalous synchrophasor signatures in high-volume streaming data by proposing a transfer learning-based Hoeffding tree method, achieving a computational time saving of 0.7ms (0.34ms vs. 1.04ms) while maintaining 94% accuracy for fault detection.
Synchrophasor technology or phasor measurement units (PMUs) are known to detect multiple type of oscillations or faults better than Supervisory Control and Data Acquisition (SCADA) systems, but the volume of Bigdata (e.g., 30-120 samples per second on a single PMU) generated by these sensors at the aggregator level (e.g., several PMUs) requires special handling. Conventional machine learning or data mining methods are not suitable to handle such larger streaming realtime data. This is primarily due to latencies associated with cloud environments (e.g., at an aggregator or PDC level), and thus necessitates the need for local computing to move the data on the edge (or locally at the PMU level) for processing. This requires faster real-time streaming algorithms to be processed at the local level (e.g., typically by a Field Programmable Gate Array (FPGA) based controllers). This paper proposes a transfer learning-based hoeffding tree with ADWIN (THAT) method to detect anomalous synchrophasor signatures. The proposed algorithm is trained and tested with the OzaBag method. The preliminary results with transfer learning indicate that a computational time saving of 0.7ms is achieved with THAT algorithm (0.34ms) over Ozabag (1.04ms), while the accuracy of both methods in detecting fault events remains at 94% for four signatures.