Hierarchical Temporal Memory Based Machine Learning for Real-Time, Unsupervised Anomaly Detection in Smart Grid: WiP Abstract

Micro-phasor measurement unit (μPMU) sensors in smart electric grids provide measurements of voltage and current at microsecond timescale across the network and have great potential value for grid diagnostics. In this work, we propose a novel neuro-cognitive inspired architecture based on Hierarchic...

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Bibliographic Details
Published in:ACM/IEEE International Conference on Cyber-Physical Systems (Online) pp. 188 - 189
Main Authors: Barua, Anomadarshi, Muthirayan, Deepan, Khargonekar, Pramod P., Al Faruque, Mohammad Abdullah
Format: Conference Proceeding
Language:English
Published: IEEE 01.04.2020
Subjects:
Anomaly detection
Computer architecture
cortical learning algorithm
cyber-physical systems
hierarchical temporal memory
Neurons
Phasor measurement units
Real-time systems
Sensors
Smart grids
sparse distributed representations
ISSN:2642-9500
Online Access:Get full text
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Summary:Micro-phasor measurement unit (μPMU) sensors in smart electric grids provide measurements of voltage and current at microsecond timescale across the network and have great potential value for grid diagnostics. In this work, we propose a novel neuro-cognitive inspired architecture based on Hierarchical Temporal Memory (HTM) for real-time anomaly detection in smart grid using μPMU data. The key technical idea is that the HTM learns a sparse distributed temporal representation of sequential data that turns out to be very useful for anomaly detection in real-time.Our numerical results show that the proposed HTM architecture can predict anomalies with 96%, 96%, and 98% accuracy for three different application profiles namely, Standard, Reward Few False Positive, Reward Few False Negative, respectively. The performance is compared with three state-of-the-art real-time anomaly detection algorithms and HTM demonstrates competitive score for real-time anomaly detection in μPMU data.
ISSN:2642-9500
DOI:10.1109/ICCPS48487.2020.00027