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Applied Sciences
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Analysis, Modelling and Simulation in Electrical Power Systems
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Analysis, Modelling and Simulation in Electrical Power Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 4474

Special Issue Editor

Prof. Dr. Qiuqin Sun

E-Mail Website
Guest Editor
College of Electrical & Information Engineering, Hunan University, Changsha 410082, China
Interests: electromagnetic transients in power system; high voltage engineering; renewable energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the continuous development and advancement of electrical power systems, there is a growing demand for the analysis, modelling, and simulation of these systems. These efforts are crucial for optimizing system operation and improving the reliability, safety, and efficiency of electrical power systems. The aim is to explore the latest research findings, technological innovations, and practical experiences in the field of electrical power systems, providing a platform for exchange, learning, and collaboration for both the academic and engineering communities.

Potential topics include, but are not limited to, the following:

  • Methods and techniques for power system analysis;
  • Power system modelling and simulation tools;
  • Modelling of smart grids and microgrids;
  • Integration of renewable energy and power system simulation;
  • Power system stability and control;
  • Fault diagnosis and recovery in power systems;
  • Power markets and energy management systems;
  • Data analysis and big data applications in power systems;
  • Security and protection techniques for power systems;
  • Other topics related to analysis, modelling, and simulation in electrical power systems.

Prof. Dr. Qiuqin Sun
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. Applied Sciences 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 2400 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 system analysis
  • power system modelling
  • power system simulation
  • big data application
  • artificial intelligence

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Published Papers (3 papers)

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Research

16 pages, 1534 KiB  
Article
Impact of Twisting on Skin and Proximity Losses in Segmented Underground Cables: A 3D Finite-Element Study
by Soheil Ahmadi, S. H. Khan and K. T. V. Grattan
Appl. Sci. 2025, 15(5), 2814; https://doi.org/10.3390/app15052814 - 5 Mar 2025
Cited by 1 | Viewed by 500
Abstract
This paper presents a comprehensive three-dimensional (3D) finite-element (FE) study of skin and proximity losses in a five-segment, helically twisted underground power cable. Unlike conventional two-dimensional (2D) analyses—which assume parallel conductors and consequently overestimate eddy current losses—our 3D approach accurately captures the effects [...] Read more.
This paper presents a comprehensive three-dimensional (3D) finite-element (FE) study of skin and proximity losses in a five-segment, helically twisted underground power cable. Unlike conventional two-dimensional (2D) analyses—which assume parallel conductors and consequently overestimate eddy current losses—our 3D approach accurately captures the effects of varying lay lengths (λ). Simulations are performed from 0 Hz (DC) to 50 Hz, showing that while the per-unit-length DC resistance remains unaffected by twisting, the AC resistance can increase significantly depending on the pitch. At 50 Hz, the ratio of AC to DC resistance (RAC/RDC) ranges from about 1.32 for very tight twists (λ=0.1m) to nearly 1.72 for gentle pitches (λ=5.0m). Further analysis reveals that short lay lengths enhance magnetic field coupling, improving current distribution and partially mitigating losses. To quantify these findings, an exponential-saturation model is proposed to describe RAC/RDC as a function of lay length, achieving excellent agreement (R2≈0.996) with the 3D FE data. These results underscore the importance of considering full 3D geometry in cable design, offering a practical tool for optimizing both mechanical reliability and electromagnetic performance in high-voltage underground applications. Full article
(This article belongs to the Special Issue Analysis, Modelling and Simulation in Electrical Power Systems)
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31 pages, 10502 KiB  
Article
Flexible Simulation Platform for Generating Realistic Waveforms with Voltage Notches
by Joaquín E. Caicedo, Olga Zyabkina, Edwin Rivas and Jan Meyer
Appl. Sci. 2024, 14(23), 11031; https://doi.org/10.3390/app142311031 - 27 Nov 2024
Viewed by 692
Abstract
Voltage notches are steady-state sub-cycle waveform distortions caused by the normal operation of line-commutated power converters, significantly impacting power quality in industrial low-voltage (LV) networks. Despite their common occurrence, research on this phenomenon is still incipient, and realistic simulation platforms are lacking. This [...] Read more.
Voltage notches are steady-state sub-cycle waveform distortions caused by the normal operation of line-commutated power converters, significantly impacting power quality in industrial low-voltage (LV) networks. Despite their common occurrence, research on this phenomenon is still incipient, and realistic simulation platforms are lacking. This paper introduces a detailed MATLAB (R2024a)/Simulink-based simulation platform that models a benchmark low-voltage industrial installation, including a six-pulse controlled rectifier, linear loads, and a capacitor bank for power factor correction. Systematic simulations are performed with the platform to examine the sensitivity of notch characteristics to key parameters within plausible ranges, such as short-circuit power at the point of common coupling, commutation reactance, firing angle, snubber circuits, and rated power of the rectifier. In addition, parameters such as the rated power of linear loads and the compensation power of the capacitor bank are examined. Other influencing parameters including background voltage unbalance and distortion are also modeled and considered. A comparative analysis with field measurements from German industrial LV networks validates the plausibility and suitability of the simulations. Building upon this platform, a Monte Carlo simulation approach is adopted to generate extensive datasets of realistic voltage notch waveforms by randomly varying these key parameters. A case study conducted under conditions typical of German LV networks demonstrates the applicability of the simulations. To support further research, the simulation platform and exemplary synthetic waveforms are provided alongside the paper, serving as a valuable tool for testing and designing strategies for analysis, detection, and monitoring of voltage notches. Full article
(This article belongs to the Special Issue Analysis, Modelling and Simulation in Electrical Power Systems)
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16 pages, 6520 KiB  
Article
Classification of Faults in Power System Transmission Lines Using Deep Learning Methods with Real, Synthetic, and Public Datasets
by Ozan Turanlı and Yurdagül Benteşen Yakut
Appl. Sci. 2024, 14(20), 9590; https://doi.org/10.3390/app14209590 - 21 Oct 2024
Cited by 2 | Viewed by 2894
Abstract
Every part of society relies on energy systems due to the growing population and the constant demand for energy. Because of the high energy demands of transportation, industry, and daily life, energy systems are crucial to every part of society. Electrical transmission lines [...] Read more.
Every part of society relies on energy systems due to the growing population and the constant demand for energy. Because of the high energy demands of transportation, industry, and daily life, energy systems are crucial to every part of society. Electrical transmission lines are a crucial component of the electrical power system. Therefore, in order to determine the power system’s protection plan and increase its reliability, it is critical to foresee and classify fault types. With this motivation, the main goal of this paper is to design a deep network model to classify faults in transmission lines based on real, generated, and publicly available datasets. A deep learning architecture that was based on a one-dimensional convolutional neural network (CNN) was utilized in this study. Accuracy, specificity, recall, precision, F1 score, ROC curves, and AUC were employed as performance criteria for the suggested model. Not only synthetic but also real data were used in this study. It has been seen that the created model can be used successfully for both real data and synthetic data. In order to measure the robustness of the network, it was tested with three different datasets consisting of real, generated, and publicly available datasets. In the paper, 1D CNN, one of the machine learning methods, was used on three different power systems, and it was observed that the machine learning method was successful in all three power systems. Full article
(This article belongs to the Special Issue Analysis, Modelling and Simulation in Electrical Power Systems)
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