Abstract
The huge volume of data that is streamed from distribution phasor measurement units (DPMU) toward distribution management system (DMS) can rarely be used in the raw format. Data-driven analysis to extract information out of massive raw data has revealed promising opportunities to overcome this challenge. This paper utilizes convolutional autoencoders (Conv-AE) for the sake of anomaly detection based on the DPMU measurements in distribution systems. The Conv-AE is unsupervised and independent of the event type. It dispenses preprocessing and feature extraction along with obtaining the essential information directly from the measured data. The anomaly detection is followed by a supervised classifier which is developed to identify the type of anomaly. This classifier is a convolutional neural network that is designed and fine-tuned for the problem at hand. It uses an ensemble learning method to augment the fully-labelled dataset. Performance of the proposed methodology is evaluated on modified IEEE 34-node and IEEE 123-node test feeders in various operating conditions, presence of noise, and different scenarios of DPMU outage. Moreover, a real-world DPMU dataset is utilized to evaluate performance of the Conv-AE model in practical conditions. Results confirmed effectiveness of the proposed technique to be used in future DMS platforms.
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