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.

Incorporating new business and trading models in the process of hosting capacity (HC) analyses of distribution systems is of great importance. Accordingly, this paper presents a two-stage methodology to investigate the effects of flexibility contracts (FlexCons) on the HC assessment of distribution grids. In the first stage, the maximization of FlexCon exchanges between photovoltaic (PV) solar owners and demands is addressed. In the second stage, an optimization-based HC calculation is carried out for various cases with and without considering FlexCons. In particular, the impacts of location and number of FlexCons on the HC are studied. The results of simulations on a 33-bus radial distribution network indicate an increase in PV-HC by considering contract-based trading of power flexibility through FlexCons.

The topology of a device will determine the impact said device has on the grid and how immune that device is for disturbances in the grid. LED lamps are very commonly used devices, with different topologies available in the market, each topology showing different behavior when connected to a grid. For power quality studies, it is important to classify LED lamps, without breaking them to know the topology. Several classification methods are found in the literature with this purpose. In this paper, four methods from different papers for classifying LED lamps have been applied to a group of 21 LED lamps with active power consumption below 25 W. It has been observed that the applicability of the methods may lead to a gap of knowledge needed for classification, leaving space for personal criteria when classifying, that can be afforded using unsupervised Machine Learning. Two unsupervised Machine Learning methods were applied using the electrical parameters and statistics proposed in literature.