Adaptive Hoeffding Tree with Transfer Learning for Streaming Synchrophasor Data Sets

TL;DR

This paper introduces an adaptive Hoeffding tree with transfer learning (THAT) for real-time anomaly detection in synchrophasor data, achieving faster processing times while maintaining high accuracy in fault detection.

Contribution

It presents a novel transfer learning-based Hoeffding tree algorithm tailored for streaming synchrophasor data, improving processing speed at the edge without sacrificing accuracy.

Findings

Achieves 0.7ms computational time saving over OzaBag.

Maintains 94% accuracy in fault detection.

Reduces latency for real-time anomaly detection.

Abstract

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.