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Intelligent Systems Supporting the Use of Energy Device and Other Complex Technical Objects, 2nd Edition

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: 11 July 2024 | Viewed by 422

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Guest Editor
Department of Energy, Faculty of Mechanical Engineering, Technical University of Koszalin, 15-17 Raclawicka St., 75-620 Koszalin, Poland
Interests: servicing process; reliability engineering and system safety; system modelling; mathematical modelling; application of mathematics; wind power plant; artificial neural networks; diagnostics information; expert system; intelligent system; knowledge base; power in energy
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Special Issue Information

Dear Colleagues,

I am inviting submissions to a Special Issue of Energies, titled “Intelligent Systems Supporting the Use of Energy Device and Other Complex Technical Objects, 2nd Edition”.

Today, it is becoming increasingly common to use solar and wind energy, both onshore and offshore. Equipment for generating renewable energy is continuously used; hence, it needs to undergo regular, efficient reliability testing. The daily costs and technological complexity of operating and maintaining energy equipment are rising. To replace and repair power equipment, research is ongoing for seeking several types of solutions and a more economical model.

Continuous repair cost reduction is necessary to effectively maintain renewable energy equipment in continuous serviceability (energy production) or operational readiness. The costs of repairs are strongly correlated with how quickly these facilities are being renewed. Only the employment of intelligent systems that support human (facility user) behavior makes the optimal and efficient renewal of technical facilities feasible. A system where the technical object is refreshed exactly when it is needed is the best way to manage technical objects. Only artificial neural network-based intelligent object detection systems are capable of offering such a system.

Studying and evaluating the resilience of renewable energy systems allows the efficient control of the best plan for producing electricity. The generated model of the operation process and the adopted reliability quantities that analytically describe this model serve as the foundation for the analytical evaluation of the reliability of technical facilities. Measures that increase the reliability, energy, financial efficiency, etc., of the power system in use can be chosen using knowledge of its existing reliability.

Currently, cognitive aspects in the use of intelligent systems supporting the use and operation of technical objects are particularly important, especially in the field of modeling of operational processes of the technical objects being tested as well as research, evaluation, and analysis of the reliability of operational processes of objects that use intelligent systems.

Prof. Dr. Stanisław Duer
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

  • artificial intelligence in energy systems
  • modeling of technical objects
  • expert systems
  • reliability and operation process
  • security and safety systems
  • diagnostics of technical objects
  • renewable energy sources
  • three-phase power grid
  • transport systems
  • wind farm devices
  • power in energy

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Published Papers (1 paper)

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Research

19 pages, 4723 KiB  
Article
Infrastructure Diagnosed by Solar Power Supply in an Intelligent Diagnostic System in Five-Valued Logic
by Stanisław Duer, Marek Woźniak, Jacek Paś, Marek Stawowy, Krzysztof Rokosz, Dariusz Bernatowicz, Radosław Duer and Atif Iqbal
Energies 2024, 17(10), 2408; https://doi.org/10.3390/en17102408 - 17 May 2024
Viewed by 272
Abstract
This article discusses the issue of diagnosing low-power solar power plants using the five-valued (5VL) state evaluation {4, 3, 2, 1, 0}. We address in depth how the 5VL diagnostics built upon 2VL, 3VL, and 4VL—two-valued diagnostics, three-valued logistics, and four-valued diagnostics. Logic [...] Read more.
This article discusses the issue of diagnosing low-power solar power plants using the five-valued (5VL) state evaluation {4, 3, 2, 1, 0}. We address in depth how the 5VL diagnostics built upon 2VL, 3VL, and 4VL—two-valued diagnostics, three-valued logistics, and four-valued diagnostics. Logic (5VL) assigns five state values to the range of signal value changes, and these states are completely operational ({4}), incomplete ({3}), critical efficiency ({2}), and pre-fault efficiency ({1}). For the identical ranges of diagnostic signal values, all three of the applied state valence logics interpret failure as changes outside of their permitted ranges. Diagnostic procedures made use of an AI-based DIAG 2 system. This article’s goal is to provide a comprehensive overview of the DIAG 2 intelligent diagnostic system, including its architecture, algorithm, and inference rules. Diagnosis with the DIAG 2 system is based on a well-established technique for comparing diagnostic signal vectors with reference signal vectors. A differential vector metric is born out of this examination of vectors. The input cells of the neural network implement the challenge of signal analysis and comparison. It is then possible to classify the object components’ states in the neural network’s output cells. Based on the condition of the object’s constituent parts, this approach can signal whether those parts are working, broken, or urgently require replacement. Full article
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