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Advances in the Application of Methods Based on Artificial Intelligence...
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Advances in the Application of Methods Based on Artificial Intelligence and Optimization in Power Engineering

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (10 June 2024) | Viewed by 3600

Special Issue Editor

Prof. Dr. Paweł Pijarski

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka St. 38D, 20–618 Lublin, Poland
Interests: power system analysis; electrical power engineering; heuristic optimization; metaheuristic; distributed generation; renewable energy systems; short-circuit calculations in the power system; OPF; SCOPF, artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The purpose of the research area under consideration is to identify the possibilities and determine the advisability of using various methods based on artificial intelligence and optimization methods to solve problems in the field of power engineering. The aim of this Special Issue is to consider various real and, above all, up-to-date problems currently occurring in the power system, which can be solved using modern methods. Today's power systems abound in all kinds of problems. They appear both at the stage of power grid operation and in its planning. Additionally, network operators impose their own requirements resulting from the specific nature of the network operation. All this makes it necessary to use more and more advanced methods to solve problems. Examples of such methods include those based on artificial intelligence and optimization methods. In this Special Issue, preference is given to papers that address the above topics and describe them in detail. I invite you to submit your original works to the Special Issue "Advances in the Application of Methods Based on Artificial Intelligence and Optimization in Power Engineering". The subject area of the Special Issue may include the following selected issues (these are only selected topic proposals that can be expanded within the proposed topics):

  1. Application of various methods to solve problems in the field of electrical power engineering:
  • Artificial intelligence methods, machine learning, deep learning, neural networks, expert systems, fuzzy systems, etc.
  • Optimization methods, e.g., classical, heuristics, metaheuristics, etc.
  1. Probabilistics, statistics of data and calculation results.
  2. Various analyses of the power system including methods based on artificial intelligence and optimization. The scope of analysis may cover areas such as:
  • Transmission and distribution of electricity;
  • Generation of electricity;
  • Energy storage;
  • Reliability;
  • Forecasting;
  • Power quality;
  • Faults;
  • Planning and development;
  • Operation;
  • Economic issues;
  • The impact of sources, energy storage, loads and other elements on the operation of the power grid.

Prof. Dr. Paweł Pijarski
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 engineering
  • power system
  • artificial intelligence
  • neural networks
  • optimization
  • RES
  • probabilistics
  • statistics

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Published Papers (2 papers)

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Research

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16 pages, 4551 KiB  
Article
Machine Learning Classifier for Supporting Generator’s Impedance-Based Relay Protection Functions
by Petar Sarajcev and Dino Lovric
Energies 2024, 17(8), 1820; https://doi.org/10.3390/en17081820 - 10 Apr 2024
Viewed by 686
Abstract
Transient stability of the electric power system still heavily rests on a timely and correct operation of the relay protection of individual power generators. Power swings and generator pole slips, following network short-circuit events, can initiate false relay activations, with negative repercussions for [...] Read more.
Transient stability of the electric power system still heavily rests on a timely and correct operation of the relay protection of individual power generators. Power swings and generator pole slips, following network short-circuit events, can initiate false relay activations, with negative repercussions for the overall system stability. This paper will examine the generator’s underimpedance (21G) and out-of-step (78) protection functions and will propose a machine learning based classifier for supporting and reinforcing their decision-making logic. The classifier, based on a support vector machine, will aid in blocking the underimpedance protection during stable generator swings. It will also enable faster tripping of the out-of-step protection for unstable generator swings. Both protection functions will feature polygonal protection characteristics. Their implementation will be based on European practice and IEC standards. Classifier will be trained and tested on the data derived from simulations of the IEEE New England 10-generator benchmark power system. Full article
(This article belongs to the Special Issue Advances in the Application of Methods Based on Artificial Intelligence and Optimization in Power Engineering)
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Review

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42 pages, 3780 KiB  
Review
Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue
by Paweł Pijarski and Adrian Belowski
Energies 2024, 17(2), 516; https://doi.org/10.3390/en17020516 - 20 Jan 2024
Cited by 3 | Viewed by 2372
Abstract
The challenges currently faced by network operators are difficult and complex. Presently, various types of energy sources with random generation, energy storage units operating in charging or discharging mode and consumers with different operating characteristics are connected to the power grid. The network [...] Read more.
The challenges currently faced by network operators are difficult and complex. Presently, various types of energy sources with random generation, energy storage units operating in charging or discharging mode and consumers with different operating characteristics are connected to the power grid. The network is being expanded and modernised. This contributes to the occurrence of various types of network operating states in practice. The appearance of a significant number of objects with random generation in the power system complicates the process of planning and controlling the operation of the power system. It is therefore necessary to constantly search for new methods and algorithms that allow operators to adapt to the changing operating conditions of the power grid. There are many different types of method in the literature, with varying effectiveness, that have been or are used in practice. So far, however, no one ideal, universal method or methodology has been invented that would enable (with equal effectiveness) all problems faced by the power system to be solved. This article presents an overview and a short description of research works available in the literature in which the authors have used modern methods to solve various problems in the field of power engineering. The article is an introduction to the special issue entitled Advances in the Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering. It is an overview of various current problems and the various methods used to solve them, which are used to cope with difficult situations. The authors also pointed out potential research gaps that can be treated as areas for further research. Full article
(This article belongs to the Special Issue Advances in the Application of Methods Based on Artificial Intelligence and Optimization in Power Engineering)
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