Waveform distortion in general represents a widespread problem in electrified transports due to interference, service disruption, increased losses, and aging 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 article proposes an application of unsupervised DL to measurements of railway pantograph quantities to identify waveform distortion patterns. Data consist 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 subsecond intervals. Results are explained among others by connecting the distribution of the clusters (determined by the self-supervised method) to the dynamic operating conditions of the rolling stock. The resulting DAE performance is superior in terms of accuracy and comprehensiveness of spectral components compared to a more traditional principal component analysis (PCA) that was chosen as a reference for comparison.

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.

The world is facing its most significant public health problem of the century. A COVID-19 pandemic that has spread rapidly in many different regions and countries, such as South Africa, affects health systems, economies, and societies. The South African electricity sector was also affected by the pandemic, experiencing shocks in supply and demand, putting further pressure on the country's energy systems. In this context, this study explored the preliminary impacts caused by the COVID-19 pandemic in the South African electricity sector. Such consequences resulting from the containment measures and changes in the behavior of electricity use were investigated during the seven weeks after the beginning of the country's lockdown. To achieve the aim of this paper, exploratory research was carried out, given that the phenomenon is at an early stage, making use of three secondary research methods. The results show the severe drops in electricity consumption and peak demand, which reached 28.1% and 20.2%, respectively. Finally, it is presented the challenges faced by the South African electricity sector in the context of the pandemic, highlighting the possible ways to be traced to overcome these barriers in the post-pandemic, focusing on policy structures of the country's electricity sector.

The advance of the distributed generation in Brazil makes it essential to investigate the applications and transformations that the use of these new arrangements may entail. The use of non-centralized generation technologies associated with energy storage is interesting for several sectors of the energy market, even if the market is in the process of maturing these technologies. In the context of the time-of-use rate, these changes have allowed the consumer to use strategies to save energy bill costs, especially when its moment of most considerable consumption coincides with that of the highest tariff. In this paper, a Battery Energy Storage System (BESS) is used to perform commercial peak load reduction in a microgrid in connected mode. The microgrid also has a Photovoltaic (PV) Generator Farm as Renewable Energy Sources (RES) to provide load consumption and also to assist BESS in the peak shaving operation. The modeling and simulation of the system are performed by MATLAB/Simulink. The analysis demonstrates that the peak load reduction produces the expected financial benefits under a Brazilian time-of-use rate known as White Rate, in addition to carrying out the operation in a manner consistent with the technique from an electrical point of view. The software Homer Grid validates the potential savings. Thus, the results showed that the use of energy storage associated with renewable generation under a peak shaving strategy allows greater freedom for the consumer in the face of costs with main grid purchases.

The objective of this paper was to examine the dynamic impedance estimation of electrical systems from online measurements. The paper makes several considerations and highlights the challenges to obtain a precise estimation. Transducer equalization and harmonic synchrophasor estimation (HSpE) are reviewed and discussed. The use of online and adaptive equalization for transducers proves to be a viable solution for improving voltage transducer’s (VT’s) and current transducer’s (CT’s) frequency response. Additionally, the use of oversampling algorithms can mitigate the effects of noise in the HSpE. Furthermore, methods for harmonic impedance estimation are discussed. The independent component analysis ICA-based dynamic impedance estimation is proposed and results presented, which yields excellent agreement. Finally, harmonic modeling and simulation of injected harmonic currents are used to observe resonances through the amplification and attenuations and, consequently, the opportunity to confirm the system self and transfer impedances of a test system. Dynamic impedance estimation will continue to be a great challenge for the power systems engineer as the system complexity increases with the massive insertion of power electronic inverters and the associated required filtering. Real-time signal processing will be an effective tool to determine the dynamic self or transfer impedance.