Anomaly-Aware Adaptive Sampling for Electrical Signal Compression

Intelligent electronic devices for power systems often entail high frequency sampling of electric signals, enabled to capture anomalous signal behavior. However, in normal operation this oversampling is redundant and leads to excessive data being stored or transmitted. This gives rise to a new compression problem where the collected samples should be further subsampled and quantized based on the presence of an anomaly in the underlying signal. We propose an Anomaly-aware Compressive Sampler (ACS) which tests the signal for the presence of an anomaly in a block of samples, and subsamples in a hierarchical manner to retain the desired sampling rate. ACS has been designed keeping hardware constraints in mind, using integer operations, an appropriate bit-packing, a simple iterated delta filter, and a streaming data pipeline. We present a mathematical formulation of the problem and analyze the performance of ACS, establishing theoretically its ability to identify anomalies in the signal and adapt the sampling rate. ACS competes with the state-of-the-art algorithm for the better-behaved transmission system data from DOE/EPRI, and outperforms it significantly on real-time distribution system data recorded in our laboratory. Finally, ACS is lightweight and was implemented on an ARM processor.