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: 10 June 2024 | Viewed by 2397
Special Issue Editor
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):
- 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.
- Probabilistics, statistics of data and calculation results.
- 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
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Optimal reconfiguration of network operation and power reduction in renewable energy sources to eliminate overloads of power lines and transformers
Authors: Paweł Pijarski; Candra Saigustia; Piotr Kacejko; Adrian Belowski
Affiliation: Lublin University of Technology, Poland
Title: An ANN-based method of voltage control in LV networks with a large share of photovoltaics - comparative analysis
Authors: Klara Janiga; Piotr Miller
Affiliation: Poland
Title: Machine learning classifier for supporting generator's impedance-based relay protection functions
Authors: Petar Sarajcev; Dino Lovric
Affiliation: University of Split
Abstract: Transient stability of the electric power system still heavily rests on the 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.