Railway electrified systems are one of the most popular and essential forms of transportation globally, and the performance of those systems impacts society. The electric railway power systems (ERPS) comprehend the infrastructure and apparatus that aims to deliver power for the rolling stocks in different types of railway transportation. Due to the broad application of static power electronics, ERPS is characterized by several sources of waveform distortion. Waveform distortion is a critical power quality (PQ) issue and a challenge to managing electromagnetic compatibility (EMC) in railway systems. It englobes harmonics (disturbances synchronous with the fundamental power frequency up to 2 kHz), interharmonics (disturbances asynchronous with the fundamental power frequency up to 2 kHz), and supraharmonics (synchronous and asynchronous disturbances between 2 and 150 kHz).

The ERPS has several system complexities that should be taken into consideration when assessing waveform distortion related to the characteristics of the phenomena: extensive distribution system with intricate circuit arrangements and moving single-phase loads; multiple voltage levels and electromagnetic environments, including railway grid and subsystems, as well as public grid; waveform distortion has time-varying behavior dependent on operating states of rolling stock, traffic plan, grid balancing, and spatial position of the vehicles; a mix between traditional equipment or infrastructure and population of new power electronic conversion stages with a lack of guidelines and standardization; and variety of waveform distortion sources and signatures.

The objective of this research is to gain knowledge and a better understanding of waveform distortion, including not only harmonics but also interharmonics and supraharmonics in railways systems, to characterize emission sources, propagation, the impact of the operation on time-varying behaviors in several scales, interaction among systems and subsystems, and adverse effects. The focus of the work is alternating current (AC) electrified railways, with a deeper assessment of, but not limited to, the railway system solution of Sweden (15 kV 16 ⅔ Hz). The development and scope of this work provide a comprehensive literature review of waveform distortion assessment for electrical railway power systems and build up a framework for future contributions, characterization of waveform distortion for electrical railway power systems using measurements, conduct detailed measurements on waveform distortion in a traction converter station, modeling waveform distortion propagation for ERPS considering complexities of the system, application of unsupervised deep learning (DL) methods to find patterns in waveform distortion data and investigation of the impacts related with those issues. The research contributions from those defined scopes are summarized below.

·         Identification of the challenges of waveform distortion assessment in ERPS and categorizing the available literature to address some of those challenges.

·         Characterization and screening of the waveform distortion time-varying dependencies in different time scales.

·         Providing a methodology for assessing time-varying waveform distortion in railway systems, adapting traditional methodologies, advanced statistical analysis, and machine learning approaches.

·         Modeling waveform distortion interaction within the ERPS in Sweden, incorporating challenges such as moving loads, meshed grid analyses, and a wide range of disturbances propagation in ERPS.

·         Addressing the different mechanisms affecting waveform distortion at the catenary and public grid sides.

·         Investigation of the impact of waveform distortion performance on associated equipment.

The work provides crucial steps for better establishing a PQ framework and future standardization for waveform distortion in ERPS by exploring multiple aspects and directions on the assessment side.

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.

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.