Fakulta informatiky a managementu http://hdl.handle.net/20.500.12603/44 2026-07-21T18:11:38Z 2026-07-21T18:11:38Z Aboelezz, Asmaa M. El-Saadawi, Magdi M. Eladl, Abdelfattah A. El-Afifi, Magda I. Bureš, Vladimír Sedhom, Bishoy E. http://hdl.handle.net/20.500.12603/2848 2026-07-21T06:46:43Z 2025-01-01T00:00:00Z

Improved protection scheme for shipboard microgrids based on high frequency impedance method with experimental validation Aboelezz, Asmaa M.; El-Saadawi, Magdi M.; Eladl, Abdelfattah A.; El-Afifi, Magda I.; Bureš, Vladimír; Sedhom, Bishoy E. his paper addresses the critical problem of fault detection in DC zonal shipboard microgrids, which is essential for ensuring system reliability and operational safety. The proposed method detects faults by utilizing the high-frequency characteristics of estimated impedance. The technique involves Fast Fourier Transform analysis of current and voltage waveforms to extract high-frequency components before and after a fault. These features help identify the system’s high-frequency impedance via a communication system. Fault detection is achieved by comparing the estimated impedance with a predefined threshold. The performance of the method is evaluated using MATLAB/Simulink simulations and experimental implementations under various scenarios. Communication between components in the DC zonal microgrid is managed using the IEC 61850 standard. Results demonstrate the method’s effectiveness in detecting faults under diverse conditions, including variations in fault resistance, dynamic load behavior, changes in photovoltaic irradiation, system configuration alterations, noise immunity, and multi-fault scenarios. The method achieves fault clearance times ranging from 0.17 ms in MATLAB/Simulink simulations to 33–45 ms in experimental tests, showing its capability to enhance fault detection in complex DC microgrid environments.

2025-01-01T00:00:00Z Borkovcová, Anna Černá, Miloslava Sokolová, Marcela http://hdl.handle.net/20.500.12603/2845 2026-07-21T06:46:21Z 2022-01-01T00:00:00Z

Blockchain in the Energy Sector-Systematic Review Borkovcová, Anna; Černá, Miloslava; Sokolová, Marcela The article provides an overview of academic contributions to blockchain technology over the past three years. A large number of practical implementations are proving the versatility of blockchain across industries. Some of these areas are easy to deduce, but for some, the benefits of using blockchain technology may not be obvious. Real applications of blockchain can be found in sectors such as cyber security and the financial sector, but also in various categories of the public sector, healthcare, and industry. This paper focuses on the use of blockchain technology in the energy industry. The paper aims to present the current trends of blockchain in the energy sector and provide a summary of blockchain technology discussed in academia. The research questions are formulated to correspond to the basic goals of the energy sector today. The core of the paper forms a systematic review based on the PRISMA guidelines. The output of this systematic review brings an up-to-day insight into the issue and introduces potential areas for further research.

2022-01-01T00:00:00Z Ghoushchi, Saeid Jafarzadeh Jalalat, Sepideh Miralizadeh Bonab, Shabnam Rahnamay Ghiaci, Ali Memarpour Haseli, Gholamreza Tomášková, Hana http://hdl.handle.net/20.500.12603/2842 2026-07-21T06:45:57Z 2022-01-01T00:00:00Z

Evaluation of Wind Turbine Failure Modes Using the Developed SWARA-CoCoSo Methods Based on the Spherical Fuzzy Environment Ghoushchi, Saeid Jafarzadeh; Jalalat, Sepideh Miralizadeh; Bonab, Shabnam Rahnamay; Ghiaci, Ali Memarpour; Haseli, Gholamreza; Tomášková, Hana Accurately recognizing potential failures in the early stages of providing products or services can prevent the loss of investment and time and reduce the risk of safety hazards. Failure mode and effects analysis (FMEA) is a conventional approach for detecting and prioritizing the probable failures of a product's design or production process. Nevertheless, the traditional risk priority number (RPN) method has come under criticism for its deficiencies. This paper proposes a modified FMEA method based on fuzzy Multi-Criteria Decision Making (MCDM) techniques to cope with the weaknesses of the previous methodologies and improve the primary method. The concept of spherical fuzzy sets (SFS) is utilized to address the vagueness and impreciseness of the information that allows the experts to have more freedom in making decisions by including membership, non-membership, and hesitation of fuzzy sets. Initially, the procedure of assigning weights to the RPN criteria is implemented with SFS step-wise weight assessment ratio analysis (SWARA). Then, the failure modes are ranked by the SFS combined compromise solution (CoCoSo) method. The effectiveness and practicality of the suggested approach are illustrated through a case study on the Manjil wind farm in Iran. Results show that the suggested model is more reliable and realistic to be utilized in the prioritization of failures than the common FMEA method or other integrated MCDM approaches.

2022-01-01T00:00:00Z Chiu, Po Chan Selamat, Ali Bin Krejcar, Ondřej Kuok, King Kuok Bujang, Siti Dianah Abdul Fujita, Hamido http://hdl.handle.net/20.500.12603/2841 2026-07-21T06:45:52Z 2022-01-01T00:00:00Z

Missing Value Imputation Designs and Methods of Nature-Inspired Metaheuristic Techniques: A Systematic Review Chiu, Po Chan; Selamat, Ali Bin; Krejcar, Ondřej; Kuok, King Kuok; Bujang, Siti Dianah Abdul; Fujita, Hamido Missing values are highly undesirable in real-world datasets. The missing values should be estimated and treated during the preprocessing stage. With the expansion of nature-inspired metaheuristic techniques, interest in missing value imputation (MVI) has increased. The main goal of this literature is to identify and review the existing research on missing value imputation (MVI) in terms of nature-inspired metaheuristic approaches, dataset designs, missingness mechanisms, and missing rates, as well as the most used evaluation metrics between 2011 and 2021. This study ultimately gives insight into how the MVI plan can be incorporated into the experimental design. Using the systematic literature review (SLR) guidelines designed by Kitchenham, this study utilizes renowned scientific databases to retrieve and analyze all relevant articles during the search process. A total of 48 related articles from 2011 to 2021 were selected to assess the review questions. This review indicated that the synthetic missing dataset is the most popular baseline test dataset to evaluate the effectiveness of the imputation strategy. The study revealed that missing at random (MAR) is the most common proposed missing mechanism in the datasets. This review also indicated that the hybridizations of metaheuristics with clustering or neural networks are popular among researchers. The superior performance of the hybrid approaches is significantly attributed to the power of optimized learning in MVI models. In addition, perspectives, challenges, and opportunities in MVI are also addressed in this literature. The outcome of this review serves as a toolkit for the researchers to develop effective MVI models.

2022-01-01T00:00:00Z