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Analytics of Waveform Distortion Variations in Railway Pantograph Measurements by Deep Learning
Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Energivetenskap.ORCID-id: 0000-0002-3625-8578
Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Energivetenskap.ORCID-id: 0000-0001-5845-5620
Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Energivetenskap.ORCID-id: 0000-0002-4004-0352
Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, 16145 Genoa, Italy.ORCID-id: 0000-0002-0096-7305
2022 (Engelska)Ingår i: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 71, artikel-id 2516211Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Waveform distortion in general represent a widespread problem in electrified transports due to interference, service disruption, increased losses and ageing of components. Given the multitude of moving sources and the extremely variable operating conditions, short time records must be considered for analysis, and this increases in turn its complexity, from which the need for effective automated processing, as offered by a deep learning (DL) approach. This paper proposes an application of unsupervised DL to measurements of railway pantograph quantities to identify waveform distortion patterns. Data consists of pantograph current from a Swiss 15 kV 16.7 Hz railway system. Three DL input types are considered: waveforms, harmonic spectra, and supraharmonic spectra. The applied DL method applied is the deep autoencoder (DAE) followed by feature clustering, using techniques to define a suitable number of clusters. Short-term distortion is evaluated over sub-10 min intervals of harmonic and supraharmonic spectra down to sub-second intervals. Results are explained among others by connecting the distribution of the clusters (determined by self-supervised method) to the dynamic operating conditions of the rolling stock. Resulting DAE performance are superior in terms of accuracy and comprehensiveness of spectral components compared to a more traditional principal component analysis (PCA) that was chosen as reference for comparison.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2022. Vol. 71, artikel-id 2516211
Nyckelord [en]
Autoencoder, clustering, deep learning (DL), pattern analysis, power quality (PQ), power system harmonics, unsupervised learning
Nationell ämneskategori
Annan elektroteknik och elektronik
Forskningsämne
Elkraftteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-92467DOI: 10.1109/tim.2022.3197801ISI: 000844142300008Scopus ID: 2-s2.0-85136013724OAI: oai:DiVA.org:ltu-92467DiVA, id: diva2:1687265
Forskningsfinansiär
TrafikverketEnergimyndigheten
Anmärkning

Validerad;2022;Nivå 2;2022-09-26 (hanlid)

Tillgänglig från: 2022-08-15 Skapad: 2022-08-15 Senast uppdaterad: 2023-09-05Bibliografiskt granskad
Ingår i avhandling
1. Applications of Unsupervised Deep Learning for Analysing Time-Varying Power Quality Big Data
Öppna denna publikation i ny flik eller fönster >>Applications of Unsupervised Deep Learning for Analysing Time-Varying Power Quality Big Data
2023 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Continuous power quality monitoring allows grid stakeholders to obtain information about the performance of the network and costumer facilities. Moreover, the analysis of continuous monitoring allows researchers to obtain knowledge on power quality phenomena. Power quality measurements result in a large amount of data. Power quality data can be classified as big data, not only for its volume, but also for the other complexities: velocity, variety, and veracity. Manual analysis of power quality data is possible but time-consuming. Moreover, data reports based on standardized indexes and classical statistical techniques might hide important information of the time-varying behaviour in power quality measurements. Artificial intelligence plays a role in providing automatic tools for proper analytics of big data.  A subset of artificial intelligence called machine learning has enabled computers to learn without explicit programming. Driven by the huge improvements in computer processing, a subset of machine learning based on multiple layers of artificial neural networks has been developed to tackle increasingly complex problems. The so-called deep learning applications teach themselves to perform a specific task by automatically extracting essential features from the raw data. Despite the possibilities of automatic feature extraction, most applications of deep learning to power quality are still the same as expert systems or earliest machine learning tools. Moreover, most of the applications are based on synthetic generated data and supervised techniques. In this context, the main motivation of this thesis is providing a new tool based on unsupervised deep learning to handle analytics of time-varying power quality big data. 

The unsupervised deep learning method proposed in this thesis combines a deep autoencoder with clustering for extracting patterns and anomalies in power quality big data. The deep autoencoder maps the original data to a compressed format that contains the principal features of the data. Automatic results are provided by the deep learning, and inferences can be obtained without requiring prior knowledge of deep learning. The outputs from unsupervised deep learning can serve as a guide for further data analysis, highlighting important time steps within large power quality datasets. By following these indications from the deep learning results, experts gain valuable insights into power quality phenomena, which can be referred to as "learning from deep learning". The interpretation of the deep learning results in this thesis allowed to making proper inferences for patterns and anomalies. For power quality measurements synchronised with 24-h, the results allowed making inferences concerning daily variations, seasonality, and the origins of power quality disturbances. For power quality measurements non-synchronised with 24-h, the results could be interpreted visually through the distribution of the patterns in a physical variable, such as the dynamic operating conditions of an electrical railway power system.

An important contribution of this thesis concerns the physical interpretation of the phenomena is related to the anomalies in harmonics caused by geomagnetically induced currents. An interesting finding by applying the deep anomaly detection to measurements in the Swedish transmission grid is the damping of the anomalies caused by geomagnetically induced currents in the winter due to the heating load. This thesis also demonstrated that the signatures for anomalies in harmonic measurements in a Swedish transmission location are similar to the ones found in a low-latitude transmission location at the South Atlantic Anomaly due to geomagnetically induced currents. Moreover, by cross-checking the anomalies at the South Atlantic Anomaly with protection trips with undetermined causes, this thesis demonstrated that anomaly harmonics due to geomagnetically induced currents can cause protection mal trips.

This thesis demonstrates that unsupervised deep learning can serve as an additional tool for compressing time-varying power quality big data into a more interpretable form. Despite the application of an unsupervised method, power quality experts remain significant in power quality studies. The main conclusion of this thesis is that unsupervised deep learning enhances the understanding of power quality experts and provides a complementary approach for analysing and extracting insights from time-varying power quality big data.

Ort, förlag, år, upplaga, sidor
Luleå: Luleå University of Technology, 2023
Serie
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Nyckelord
power quality, power system harmonics, electric power distribution, interharmonics, big data analytics, pattern analysis, unsupervised learning, deep learning, artificial intelligence, geomagnetically induced currents
Nationell ämneskategori
Annan elektroteknik och elektronik
Forskningsämne
Elkraftteknik
Identifikatorer
urn:nbn:se:ltu:diva-100718 (URN)978-91-8048-353-7 (ISBN)978-91-8048-354-4 (ISBN)
Disputation
2023-10-04, Hörsal A, Luleå tekniska universitet, Skellefteå, 09:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2023-08-25 Skapad: 2023-08-24 Senast uppdaterad: 2023-09-13Bibliografiskt granskad

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Salles, Rafael S.de Oliveira, Roger AlvesRönnberg, Sarah K.

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