用迁移学习提升电力数据流的实时异常检测速度
Adaptive Hoeffding Tree with Transfer Learning for Streaming Synchrophasor Data Sets
- 基于迁移学习改进霍夫丁树，实现边缘设备快速处理
- 检测准确率达94%，计算耗时减少0.7毫秒
- 适合部署在FPGA等资源受限的电力边缘节点
同步相量技术（即相量测量单元，PMUs）能比监控与数据采集系统（SCADA）更有效地检测多种振荡或故障，但单个PMU每秒产生30-120次采样数据，在聚合端（如多个PMU）生成大量实时数据，需特殊处理。传统机器学习方法难以应对此类大规模流式数据，主要因云环境存在延迟，需将处理移至边缘（如在PMU本地）。本文提出一种基于迁移学习的自适应霍夫丁树结合ADWIN（THAT）算法，用于检测异常同步相量特征。采用OzaBag方法进行训练与测试。初步结果表明，THAT算法（0.34ms）相比OzaBag（1.04ms）节省0.7ms计算时间，且在四类信号上故障检测准确率均保持94%。
原文摘要 · Abstract (English)
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.
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