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Abstract: This paper presents a novel sorting algorithm for modular multilevel inverters (MMCs) with integrated batteries, designed to ensure the uninterrupted operation of electric vehicles (EVs) under post-fault conditions. The proposed system structure consists of an MMC with four full-bridge modules per phase, where one module acts as a spinning reserve during normal operation. The algorithm addresses a single switch fault per phase by operating the faulted module in half-bridge mode, ensuring all batteries remain operational and maintaining full power output and battery capacity without any noticeable changes for the vehicle operator. Unlike conventional fault-tolerant strategies that often reduce power output or disable affected modules, the proposed algorithm isolates the faulty switch while preserving system output. This approach avoids derating and eliminates the need for immediate maintenance, enabling the EV to continue operating under fault conditions. Simulation and experimental results validate the effectiveness of the algorithm under a single switch fault scenario, demonstrating its ability to maintain autonomy and consistent power delivery. This work demonstrates a fault-tolerant MMC principle, offering a robust and scalable solution for enhancing reliability and user satisfaction in EV power systems.
Keywords: modular multilevel converter, fault tolerance, redundancy, uninterrupted operation, sorting algorithm
Published in DKUM: 09.04.2025; Views: 0; Downloads: 4
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Abstract: Evolutionary algorithms have been shown to be very effective in solving complex optimization problems. This has driven the research community in the development of novel, even more efficient evolutionary algorithms. The newly proposed algorithms need to be evaluated and compared with existing state-of-the-art algorithms, usually by employing benchmarks. However, comparing evolutionary algorithms is a complicated task, which involves many factors that must be considered to ensure a fair and unbiased comparison. In this paper, we focus on the impact of stopping criteria in the comparison process. Their job is to stop the algorithms in such a way that each algorithm has a fair opportunity to solve the problem. Although they are not given much attention, they play a vital role in the comparison process. In the paper, we compared different stopping criteria with different settings, to show their impact on the comparison results. The results show that stopping criteria play a vital role in the comparison, as they can produce statistically significant differences in the rankings of evolutionary algorithms. The experiments have shown that in one case an algorithm consumed 50 times more evaluations in a single generation, giving it a considerable advantage when max gen was used as the stopping criterion, which puts the validity of most published work in question.
Keywords: evolutionary algorithms, stopping criteria, benchmarking, algorithm termination, algorithm comparison
Published in DKUM: 28.03.2025; Views: 0; Downloads: 2
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Abstract: Prompt administration of radiotherapy (RT) is one of the most effective treatments against cancer. Eachday, the radiotherapy departments of large hospitals must plan numerous irradiation sessions, con-sidering the availability of human and material resources, such as healthcare professionals and linearaccelerators. With the increasing number of patients suffering from different types of cancers, manuallyestablishing schedules following each patient’s treatment protocols has become an extremely difficultand time-consuming task. We propose an optimization algorithm that automatically schedules andgenerates patient appointments. The model can rearrange fixed appointments to accommodate urgentcases, enabling hospitals to schedule appointments more efficiently. It respects the different treatment Prompt administration of radiotherapy (RT) is one of the most effective treatments against cancer. Eachday, the radiotherapy departments of large hospitals must plan numerous irradiation sessions, con-sidering the availability of human and material resources, such as healthcare professionals and linearaccelerators. With the increasing number of patients suffering from different types of cancers, manuallyestablishing schedules following each patient’s treatment protocols has become an extremely difficultand time-consuming task. We propose an optimization algorithm that automatically schedules andgenerates patient appointments. The model can rearrange fixed appointments to accommodate urgentcases, enabling hospitals to schedule appointments more efficiently. It respects the different treatment.
Keywords: appointments, hospital management, optimization algorithm, patient satisfaction, planning, radiotherapy
Published in DKUM: 25.02.2025; Views: 0; Downloads: 8
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Abstract: In this paper, a comparative analysis of the effects of groundwater, seepage and hydraulic heave on the optimal design of embedded retaining walls is carried out. The optimization model for an optimal retaining wall (ORW) minimizes the total length of the retaining wall considering design constraints. The model is extended to include the probability of failure as an additional constraint. This overcomes the limitations of the partial safety factor approach, which does not fully account for uncertainties in the soil. In contrast, the reliability-based design (RBD) approach integrates these uncertainties and enables an assessment of the impact of seepage and hydraulic heave on the reliability of the structure. A real-coded genetic algorithm was used to determine optimal designs for both optimization methods. The results of the case study show that the addition of seepage (groundwater flow) to the hydrostatic conditions has a modest effect on the embedment depth. The design based on partial safety factors, which takes seepage into account, leads to a slight increase in the embedment depth of 0.94% compared to a retaining wall design that only takes the hydrostatic conditions of the groundwater into account. When designing on the basis of probability failure, the percentage increase in embedment depth due to seepage is between 2.19% and 6.41%, depending on the target probability of failure. Furthermore, the hydraulic heave failure mechanism did not increase the required embedment depth of the retaining wall, which means that the failure mechanism of rotation near the base was decisive for the design.
Keywords: embedded retaining wall, reliability-based design, partial safety factor design, optimization, genetic algorithm
Published in DKUM: 10.12.2024; Views: 0; Downloads: 11
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Abstract: Circular planar spiral coils are the most important parts of wireless power transfer systems. This paper presents the optimization of wireless power transfer coils used for wireless power transfer, which is a problem when designing wireless power transfer systems. A single transmitter coil transfers power to a single receiving side. The performance of the wireless power transfer system depends greatly on the size and shape of the wireless power transfer system. Therefore, the optimization of the coils is of the utmost importance. The main optimization parameter was the coupling coefficient between the transmitter and the receiver coil in the horizontally aligned and misaligned position. A genetic evolutionary algorithm was used to optimize the coil, according to the developed cost function. The algorithm was implemented using the MATLAB programming language. The constraints regarding the design of the coils are also presented for the problem to be analyzed correctly. The results obtained using the genetic algorithm were first verified using FEM simulations. The optimized coils were later fabricated and measured to confirm the theory.
Keywords: wireless power transfer, coil optimization, genetic algorithm, coupling coefficient measurement
Published in DKUM: 14.08.2024; Views: 77; Downloads: 9
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