This work aims to cover the measurement, modeling, and analysis of waveform distortions in railway power systems. It is focused on waveform distortion as a phenomenon that includes harmonic distortion, interharmonic distortion, and supraharmonics. A comprehensive view of the interactions of waveform distortions in railway systems is needed, together with a grid perspective of power quality incorporating all aspects, sources, propagation, requirements, and effects. It is understood that the communities interested or involved in the subject of railway power systems would benefit from an integrated overview of the literature on the complex problem of waveform distortion. The literature review is divided into four categories: characterization and measurements, modeling, the application of artificial intelligence, and specific issues. For each category of work, the contributions are highlighted, and a discussion on opportunities, gaps, and critical observations is provided. The work successfully builds a framework for the subject with two main characteristics; the review is informative and propositional, providing a road map of opportunities for future works. Some aspects and recommendations can be highlighted. Suggestions for future works and research practices on waveform distortion in electrical transportation are offered.

Assessing harmonic distortion measurements in the electric railway power systems (ERPS) requires evaluating the time-varying behavior, interactions, and performance in different time scales. This paper aims to map and assess harmonic voltage levels in 13 traction converter stations for the Swedish railway power supply system, with findings that have direct practical implications. For that, measurements from the public and railway grid sides for 69 weeks are analyzed. Statistical values are explored for the harmonic voltage spectra and total harmonic distortion (THD) variation. The public grid side measurements are investigated using 95th percentile weekly values, and performance is evaluated by comparing the recommended planning levels of IEC 61,000–3–6. The intraweek variation complements the information about the time-varying behavior of the THD. The 95th percentile, minimum daily values, and intraday variation are explored to understand the time-based behavior since there are no reference limits from standards for comparison, looking to the railway grid side. Extended analysis is placed on the railway grid side to highlight some aspects of measurement time-aggregation based on 10-min values, and time-series trend analysis is used to confirm traffic planning impact. Discussion and findings regarding railway operation, the technology deployed at the traction converter station, time-varying behavior, traffic planning impact, measurement time-aggregation, and spectra patterns were presented.

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.

This work proposes an application of unsupervised deep learning (DL) on 2-D images containing VI diagrams of measured railway pantograph quantities to find patterns in operating conditions (OCs) and waveform distortion. Measurement data consist of pantograph voltage and current measurements from a Swiss 15 kV 16.7 Hz commercial locomotive and a French 2x25 kV 50 Hz test-dedicated locomotive, containing more than 4000 records of 5-cycle snippets for each system. The variational autoencoder (VAE), followed by feature clustering, finds patterns in the input data. Each cluster captures patterns from the VI diagrams, which contain information from current and voltage waveshapes and sub-second variations. The time-domain admittance allows inference about the rolling stock (RS) operation and the waveform distortion spectra, including harmonics and supraharmonics characteristics from both RS and traction supply. The VAE successfully performs data embedding using only 16 channels in the latent space. The effectiveness of the method is quantified by means of the mean square reconstruction error (never larger than 1.5% and equal to 0.31% and 0.33% on average for the Swiss and French case, respectively). The t-SNE visualization confirms that overlapping of clusters is negligible, with a percentage of “misplaced” cluster points of 2.18% and 2.50%, again for the Swiss and French case, respectively. The computation time for the VAE prediction could be brought to some tens of ms representing a performance reference for future implementations. The proposed VI diagram assessment covers emissions for different OCs, rapid changes in power supply conditions, and background distortion caused by other trains on the same line, including line and impedance changes due to the moving load. In this perspective physical justification is found by domain knowledge integration for the identified clusters. A concluding discussion regarding advantages, limitations, and potential improvements or diversification is also included.