人工智能
机器学习
数据驱动
实时计算
人工神经网络
无监督学习
深度学习
作者
Anomadarshi Barua,Deepan Muthirayan,Pramod P. Khargonekar,Mohammad Abdullah Al Faruque
出处
期刊:International Conference on Cyber-Physical Systems
日期:2020-04-21
卷期号:: 188-189
被引量:5
标识
DOI:10.1109/iccps48487.2020.00027
摘要
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.
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