Unsupervised Segmentation and Classification of Waveform-Distortion Data Using Non-Active Current

Andrea Mariscotti (), Rafael S. Salles and Sarah K. Rönnberg
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Andrea Mariscotti: Department of Electrical, Electronic and Telecommunications Engineering, and Naval Architecture (DITEN), University of Genova, 16145 Genova, Italy
Rafael S. Salles: Electric Power Engineering Group, Luleå University of Technology, 93187 Skellefteå, Sweden
Sarah K. Rönnberg: Electric Power Engineering Group, Luleå University of Technology, 93187 Skellefteå, Sweden

Energies, 2025, vol. 18, issue 13, 1-27

Abstract: Non-active current in the time domain is considered for application to the diagnostics and classification of loads in power grids based on waveform-distortion characteristics, taking as a working example several recordings of the pantograph current in an AC railway system. Data are processed with a deep autoencoder for feature extraction and then clustered via k-means to allow identification of patterns in the latent space. Clustering enables the evaluation of the relationship between the physical meaning and operation of the system and the distortion phenomena emerging in the waveforms during operation. Euclidean distance (ED) is used to measure the diversity and pertinence of observations within pattern groups and to identify anomalies (abnormal distortion, transients, …). This approach allows the classification of new data by assigning data to clusters based on proximity to centroids. This unsupervised method exploiting non-active current is novel and has proven useful for providing data with labels for later supervised learning performed with the 1D-CNN, which achieved a balanced accuracy of 96.46% under normal conditions. ED and 1D-CNN methods were tested on an additional unlabeled dataset and achieved 89.56% agreement in identifying normal states. Additionally, Grad-CAM, when applied to the 1D-CNN, quantitatively identifies the waveform parts that influence the model predictions, significantly enhancing the interpretability of the classification results. This is particularly useful for obtaining a better understanding of load operation, including anomalies that affect grid stability and energy efficiency. Finally, the method has been also successfully further validated for general applicability with data from a different scenario (charging of electric vehicles). The method can be applied to load identification and classification for non-intrusive load monitoring, with the aim of implementing automatic and unsupervised assessment of load behavior, including transient detection, power-quality issues and improvement in energy efficiency.

Keywords: anomaly detection; deep learning; electromobility; energy efficiency; load signature; non-intrusive load monitoring; traction power systems (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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