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Energies
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The Latest Advances in Power Quality Detection, Analysis, and Optimization
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The Latest Advances in Power Quality Detection, Analysis, and Optimization

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 2773

Special Issue Editor

Dr. María Reyes Sánchez-Herrera

E-Mail Website
Guest Editor
Electrical Engineering Department, Engineering Technical High School, University of Huelva, Huelva, Spain
Interests: quality of electric power; harmonics; distortion sources; voltage imbalance; distributed generation; smartgrids

Special Issue Information

Dear Colleagues,

The progressive increase in the price of electricity and the problems associated with energy dependence on other countries and on non-renewable energy sources, means that administrations increase the regulation of energy efficiency. At the moment, the objective is mainly the reduction of the consumption. However, it is expected that, in the near future, administrations will begin to regulate energy consumption qualitatively, increasing, among other things, the quality of voltage and current waves at the input of each electricity consumer, as the way of improving the power system quality.

Thus, in the near future, the work made in the field of electrical power quality will also continuously increase its relevance. Therefore, this seems to be a good time to launch this Special Issue, called "The Latest Advances in Power Quality Detection, Analysis and Optimization", which includes works related to all aspects related to power quality:

  • The measurements necessary to assess the quality at the different points of microgrids and power systems;
  • The data analysis necessary to identify the harmonic and imbalance sources, among other non-conformities sources;
  • The different ways of eliminating the non-conformities and improve the quality of the voltage and current waves;
  • Any other work directly or indirectly related to electric power quality.

Dr. María Reyes Sánchez-Herrera
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • power quality
  • measurement
  • harmonic
  • imbalance
  • power filter

Published Papers (1 paper)

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Research

28 pages, 12334 KiB  
Article
Power Quality Analysis Based on Machine Learning Methods for Low-Voltage Electrical Distribution Lines
by Carlos Alberto Iturrino Garcia, Marco Bindi, Fabio Corti, Antonio Luchetta, Francesco Grasso, Libero Paolucci, Maria Cristina Piccirilli and Igor Aizenberg
Energies 2023, 16(9), 3627; https://doi.org/10.3390/en16093627 - 23 Apr 2023
Cited by 3 | Viewed by 2344
Abstract
The main objective of this paper is to propose two innovative monitoring methods for electrical disturbances in low-voltage networks. The two approaches present a focus on the classification of voltage signals in the frequency domain using machine learning techniques. The first technique proposed [...] Read more.
The main objective of this paper is to propose two innovative monitoring methods for electrical disturbances in low-voltage networks. The two approaches present a focus on the classification of voltage signals in the frequency domain using machine learning techniques. The first technique proposed here uses the Fourier transform (FT) of the voltage waveform and classifies the corresponding complex coefficients through a multilayered neural network with multivalued neurons (MLMVN). In this case, the classifier structure has three layers and a small number of neurons in the hidden layer. This allows complex-valued inputs to be processed without the need for pre-coding, thus reducing computational cost and keeping training time short. The second technique involves the use of the short-time Fourier transform (STFT) and a convolutional neural network (CNN) with 2D convolutions in each layer for feature extraction and dimensionality reduction. The voltage waveform perturbations taken into consideration are: voltage sag, voltage swell, harmonic pollution, voltage notch, and interruption. The comparison between the two proposed techniques is developed in two phases: initially, the simulated data used during the training phase are considered and, subsequently, various experimental measurements are processed, obtained both through an artificial disturbance generator and through a variable load. The two techniques represent an innovative approach to this problem and guarantee excellent classification results. Full article
(This article belongs to the Special Issue The Latest Advances in Power Quality Detection, Analysis, and Optimization)
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