Deep learning model to detect various synchrophasor data anomalies

High-density synchrophasors provide valuable information for power grid situational awareness, operation and control. Unfortunately, due to factors including communication instability and hardware failure, their data quality can be greatly deteriorated by anomalies. Since the anomalies can impact th...

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Bibliographic Details
Published in:IET generation, transmission & distribution transmission & distribution, 2020-12, Vol.14 (24), p.5739-5745
Main Authors: Deng, Xianda, Bian, Desong, Wang, Weikang, Jiang, Zhihao, Yao, Wenxuan, Qiu, Wei, Tong, Ning, Shi, Di, Liu, Yilu
Format: Article
Language:English
Subjects:
abnormal synchrophasor measurements
China
communication instability
convolutional neural nets
convolutional neural network model
data quality
deep learning
ENGINEERING
hardware failure
high-density synchrophasors
Jiangsu grid
learning (artificial intelligence)
pattern classification
phase measurement
phasor measurement
power engineering computing
power grids
power system measurement
Special Issue: Advanced Data-Analytics for Power System Operation, Control and Enhanced Situational Awareness
synchrophasor data anomaly classification
synchrophasor data anomaly detection
synchrophasor network
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Summary:High-density synchrophasors provide valuable information for power grid situational awareness, operation and control. Unfortunately, due to factors including communication instability and hardware failure, their data quality can be greatly deteriorated by anomalies. Since the anomalies can impact the performance of the synchrophasor applications, it is of paramount significance to propose a model to detect anomalies in synchrophasor. In this study, a convolutional neural network model is established to detect and classify the anomalies in the synchrophasor measurements. Four types of anomalies observed in actual synchrophasors including erroneous patterns, random spikes, missing points and high-frequency interferences are considered in this study. The proposed model is extensively evaluated via field-collected measurements from the synchrophasor network in Jiangsu grid, China. The superior performance of the proposed model indicates the great potential of using deep learning for the detection of abnormal synchrophasor measurements.
ISSN:1751-8687
1751-8695
DOI:10.1049/iet-gtd.2020.0526