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

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.

2. Probabilistics, statistics of data and calculation results.
3. 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

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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.

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• power engineering
• power system
• artificial intelligence
• neural networks
• optimization
• RES
• probabilistics
• statistics

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.