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Analysis, Modelling and Simulation in Electrical Power Systems

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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

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16 pages, 6520 KiB

Open AccessArticle

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

Viewed by 1132

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 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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