Applied sciences

Bulletin of the Polish Academy of Sciences Technical Sciences

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Bulletin of the Polish Academy of Sciences Technical Sciences | Early Access

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Abstract

The selection of a reference model (RM) for a Model-Reference Adaptive Control is one of the most important aspects of the synthesis process of the adaptive control system. In this paper, the four different implementations of RM are developed and investigated in an adaptive PMSM drive with variable moment of inertia. Adaptation mechanisms are based on the Widrow-Hoff rule (W-H) and the Adaptation Procedure for Optimization Algorithms (APOA). Inadequate order or inaccurate approximation of RM for the W-H rule may provide poor behavior and oscillations. The results prove that APOA is robust against an improper selection of RM and provides high-performance PMSM drive operation.
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Authors and Affiliations

Rafał Szczepański
Tomasz Tarczewski
Lech Grzesiak
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Abstract

In the paper results of the operation and efficiency of a DC-DC resonant converter with a switched capacitor topology, equipped with GaN transistors and SiC diodes are presented. Investigated problems are related to the optimization of the DC- DC power electronic converter in order to achieve miniaturization, a simplified design and high efficiency. The proposed system operates at a high frequency with low switching losses. The proposed design helps to achieve uniform heating of the transistors and diodes, as demonstrated by the results of the thermal imaging measurements. The GaN transistors are integrated in one package with dedicated gate drivers and used to simplify the drivers circuitry and increase the power density factor of the proposed device. In the high-frequency design presented in the paper, the converter is implemented without electrolytic capacitors. The results included in the paper contain waveforms recorded in the power circuit at ZVS operation when switching on the transistors. It occurs when the system operates above the frequency of current oscillations in the resonant circuit of the switched capacitor. Efficiency characteristics and a voltage gain curve of the converter versus its output power are presented as well. Results of efficiency and quality of waveforms are important because they allow to characterize the tested system for the implementation using WBG devices. The use of integrated GaN modules to minimize elements in the physical system is also unique to this model and it allows for very short dead-time use, operation in ZVS mode at low reverse-conduction losses.
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Authors and Affiliations

Robert Stala
1
Szymon Folmer
1
Andrzej Mondzik
1

  1. AGH University of Krakow
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Abstract

In this paper, a voltage control system for a PMSM motor based on the QZSDMC converter is proposed, which allows operation in both buck and boost modes as a possible method to make the drive resistant to power grid voltage sags. The authors presented a method for measuring and transforming the output voltage from QZS, enabling the use of a PI controller to control the voltage supplied to the DMC converter. The publication includes simulation and experimental studies comparing the operation of a PMSM motor powered by DMC and the proposed QZSDMC with voltage regulation. Simulation studies confirm the drive with QZSDMC resistance to voltage sags up to 80% of the rated value. Experimental studies demonstrate the correct operation of PMSM even with a power grid voltage amplitude equal to 40% of the rated value.
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Authors and Affiliations

Przemysław Siwek
Konrad Urbański
ORCID: ORCID
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Abstract

Efficiency, reliability, and durability play a key role in modern drive systems in line with the Industry 4.0 paradigm and the sustainability trend. To ensure this, highly efficient motors and appropriate systems must be deployed to monitor their condition and diagnose faults during the operation. For these reasons, in recent years, more and more research has been focused on developing new methods for fault diagnosis of permanent magnet synchronous motors (PMSMs). This paper proposes a novel hybrid method for the automatic detection and classification of PMSM stator winding faults based on combining the continuous wavelet transform (CWT) analysis of the negative sequence component of the stator phase currents with a convolutional neural network (CNN). CWT scalogram images are used as the inputs of the CNN-based interturn short circuits fault classifier model. Experimental tests were carried out to verify the effectiveness of the proposed approach under various motor operating conditions and at an incipient stage of fault propagation. In addition, the effects of the input image format, CNN structure, and training process parameters on model accuracy and classification effectiveness were investigated. The results of the experimental tests confirmed the high effectiveness of fault detection (99.4%) and classification (97.5%), as well as other important advantages of the developed method.
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Authors and Affiliations

P. Pietrzak
M. Wolkiewicz
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Abstract

Motion planning for autonomous vehicles relies heavily on perception and prediction results to find a safe, collision-free local trajectory that adheres to traffic rules. However, vehicle perception is frequently limited by occlusion, and the generation of safe local trajectories with restricted perception is a significant challenge in the field of motion planning. This paper introduces a collision avoidance trajectory planning algorithm that considers potential collision risks, within a hierarchical framework of sampling and optimization. The primary objective of this work is to generate trajectories that are safer and align better with human driver behavior while considering potential collision risks in occluded regions. Specifically, in occlusion scenarios, the state space is discretized, and a dynamic programming algorithm is used for a sampling-based search to obtain initial trajectories. Additionally, the concept of a driving risk field is introduced to describe potential collision risk elements within the human-vehicle-road environment. By drawing inspiration from graph search algorithms, potential collision risk areas are accurately described, and a cost function is proposed for evaluating potential risks in occluded regions. Drivers typically exhibit conservative and cautious driving behavior when navigating through occluded regions. The proposed algorithm not only prioritizes driving safety but also considers driving efficiency, thereby reducing the vehicle's conservativeness when passing through occlusions. The research results demonstrate that the ego vehicle can actively avoidblind spots and tends to move away from occluded regions, aligning more closely with human driver behavior.
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Authors and Affiliations

Yubin Qian
ORCID: ORCID
Chengzhi Deng
Jiejie Xu
Xianguo Qu
Zhenyu Song
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Abstract

The traditional industrial robots come with the prime mover, i.e. Electric Motors (EM) which ranges from a few hundred too few kilo watts of power ratings. However, for autonomous robotic navigation systems, we require motors which are light weighted with the aspect of high torque and power density. This aspect is very critical, when the EMs in robotic navigations are subjected to harsh high temperature survival conditions, where the sustainability of the performance metrics of the electromagnetic system of the EMs degrade with the prevailing high temperature conditions. Hence, this research work address and formulate the design methodology to develop a 630 W High Temperature PMSM (HTPMSM) in the aspect of high torque and power density, which can be used for the autonomous robotic navigation systems under high temperature survival conditions of 200°C. Two types of rotor configurations i.e. the Surface Permanent Magnet type (SPM) and the Interior Permanent Magnet type (IPM) of HTPMSM are examined for its optimal electromagnetic metrics under the temperature conditions of 200°C. The 630 W HTPMSM is designed to deliver the rated torque of 2 Nm within the volumetric & diametric constraints of D x L which comes at 80 x 70 mm at the rated speed of 3000 rpm with the survival temperature of 200°C with the target efficiency of greater than 90%. The FEM based results are validated through the hardware prototypes for both SPM and IPM types, and the results confirm the effectiveness of the proposed design methodology of HTPMSM for sustainable autonomous robotic navigation applications.
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Authors and Affiliations

M Anand
M Sundaram
P Sivakumar
A Angamuthu
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Abstract

Face sketch synthesis (FSS) is considered as an image-to-image translation problem, where a face sketch is generated from an input face photo. FSS plays a vital role in video/image surveillance-based law enforcement. In this paper, motivated by the recent success of generative adversarial networks (GAN), we consider conditional GAN (cGAN) to approach the problem of face sketch synthesis. However, despite the powerful cGAN model’s ability to generate fine textures, low-quality inputs characterized by the facial sketches drawn by artists cannot offer realistic and faithful details and have unknown degradation due to the drawing process, while high-quality references are inaccessible or even unexistent. In this context, we propose an approach based on Generative Reference Prior (GRP) to improve the synthesized face sketch perception. Our proposed model, that we call cGAN-GRP, leverages diverse and rich priors encapsulated in a pre-trained face GAN for generating high-quality facial sketch synthesis. Extensive experiments on publicly available face databases using facial sketch recognition rate and image quality assessment metrics as criteria demonstrate the effectiveness of our proposed model compared to several state-of-the-art methods.
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Authors and Affiliations

Sami Mahfoud
ORCID: ORCID
Messaoud Bengherabi
ORCID: ORCID
Abdelhamid Daamouche
ORCID: ORCID
Elhocine Boutellaa
Abdenour Hadid
ORCID: ORCID
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Abstract

In order to study the sensitivity of multiple karst cave factors on surface settlement during Tunnel Boring Machine(referred to TBM hereinafter) tunnelling, a three-dimensional numerical model is built by taking a subway project as an example and combining MIDAS GTS NX finite element software. Secondly, the influence of the radius, height, angle, vertical net distance, and horizontal distance of the karst cave on the maximum surface settlement is studied and sorted under the two working conditions of treatment and untreated using the grey correlation analysis method. Additionally, a multi-factor numerical model of the untreated karst cave is established. Finally, based on the preceding research, a multi-factor prediction model for the maximum surface settlement is proposed and tested. The results reveal that when the karst cave is not treated, the radius and height of the karst cave have a significant effect on the maximum surface settlement. After the cave treatment, the influence of the cave parameters on the maximum settlement of the surface is greatly reduced. The calculating modelcreated in this study offers excellent prediction accuracy and good adaptability.
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Authors and Affiliations

Bichang Dong
Tao Yang
ORCID: ORCID
Binbin JU
Zhongying QU
Chao Yi
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Abstract

The power sector confronts a crucial challenge in identifying sustainable and environmentally friendly energy carriers, with hydrogen emerging as a promising solution. This paper focuses on the modeling, analysis, and techno-economic evaluation of an independent photovoltaic (PV) system. The system is specifically designed to power industrial loads while simultaneously producing green hydrogen through water electrolysis. The emphasis is on utilizing renewable sources to generate hydrogen, particularly for fueling hydrogen-based cars. The study, conducted in Skikda, Algeria, involves a case study with thirty-two cars, each equipped with a 5 kg hydrogen storage tank. Employing an integrated approach that incorporates modeling, simulation, and optimization, the techno-economic analysis indicates that the proposed system provides a competitive, cost-effective, and environmentally friendly solution, with a rate of 0.239 $/kWh. The examined standalone PV system yields 24.5 GWh/year of electrical energy and produces 7584 kg/year of hydrogen. the findings highlight the potential of the proposed system to address the challenges in the power sector, offering a sustainable and efficient solution for bothelectricity generation and hydrogen production.
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Authors and Affiliations

Toufik Sebbagh
1

  1. LGMM Laboratory, University of Skikda, PoBox 26, Road of ElHadaiek, Skikda, 2100, Algeria
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Abstract

In this work, the level of influence of the posts published by famous people on social networks on the formation of the cryptocurrency exchange rate is investigated. Celebrities who are familiar with the financial industry, especially with the cryptocurrency market, or are somehow connected to a certain cryptocurrency, such as Elon Musk with Dogecoin, are chosen as experts whose influence through social media posts on cryptocurrency rates is examined. This research is conducted based on statistical analysis. Real cryptocurrency exchange rate forecasts for the selected time period and predicted ones for the same period, obtained using three algorithms, are utilized as a dataset. This paper uses methods such as statistical hypotheses regarding the significance of Spearman’s rank correlation coefficient and Pearson’s correlation. It is confirmed that the posts by famous people on social networks significantly affect the exchange rates of cryptocurrencies.
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Authors and Affiliations

Sergii Telenyk
ORCID: ORCID
Grzegorz Nowakowski
ORCID: ORCID
Olena Gavrilenko
Mykhailo Miahkyi
Olena Khalus
ORCID: ORCID
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Abstract

The knowledge of impacts and the load-bearing capacity of unstrengthened/strengthened structures is a crucial source of information about the safety of masonry buildings near deep excavations, especially in dense urban areas. Incorrect calculations made for such designs can seriously affect not only an analyzed object, but also the adjacent buildings. The safety of masonry buildings can be determined by many factors that are closely related to the hazards presented during the performance of deep excavations. These factors are at first identified and then prioritized. The AHP process in the multi-criteria analysis was used to support the decision-making process related to the verification of factors affecting the safety assessment of masonry buildings in the area of deep excavations. The proper design of building structures, including the verification of the structure strengthening near deep excavations, was found to be the most significant factor determining the safety of such buildings. The methodology for proceeding with the verification of ultimate (ULS) and serviceability (SLS) limit states in accordance with the literature data, current regulations, such as Eurocode 6 and other design standards, and know-how of the authors, described in this paper was the next stage of the discussed analysis.
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Authors and Affiliations

Radosław Jasiński
ORCID: ORCID
Izabela Skrzypczak
ORCID: ORCID
Agnieszka Leśniak
ORCID: ORCID
Eduardo Natividade
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Abstract

This paper presents the novel estimation algorithm that generates all signals of an object described by nonlinear ordinary differential equations based only on easy-to-implement measurements. Unmeasured signals are estimated by using an adaptive approach. For this purpose, a filtering equation with a continuously modified gain vector is used. Its value is determined by an incremental method, and the amount of correction depends on the current difference between the generated signal and its measured counterpart. In addition, the study takes into account the aging process of measurements and their random absence. The application of the proposed approach can be realized for any objects with a suitable mathematical description. A biochemically polluted river with an appropriate transformation of the notation of partial differential equations was chosen as an object. The results of numerical experiments are promising, and the process of obtaining them involves little computational necessity, so the approach is aimed at the needs of control implemented online.
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Authors and Affiliations

Tadeusz Kwater
ORCID: ORCID
Przemysław Hawro
ORCID: ORCID
Paweł Krutys
Marek Gołębiowski
Grzegorz Drałus
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Abstract

In the present paper, the most important aspects of computer algebra systems applications in complicated calculations for classical queueing theory models and their novel modifications are discussed. We mainly present huge computational possibilities of Mathematica environment and effective methods of obtaining symbolic results connected with most important performance characteristics of queueing systems. First of all, we investigate effective solutions of computational problems appearing in queueing theory such as: finding final probabilities for Markov chains with a huge number of states, calculating derivatives of complicated rational functions of one or many variables with the use of classical and generalized L’Hospital’s rules, obtaining exact formulae of Stieltjes convolutions, calculating chosen integral transforms used often in the above-mentioned theory and possible applications of generalized density function of random variables and vectors in these computations. Some exemplary calculations for practical models belonging both to classical models and their generalizations are attached as well.
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Authors and Affiliations

M. Ziółkowski
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Abstract

The occurrence of partial shading in solar power systems presents a substantial challenge with widespread implications, sparking extensive research, notably in the field of Maximum Power Point Tracking (MPPT).This study emphasizes the critical process of accurately tracking the maximum power points with the characteristic curves of Photovoltaic (PV) modules under real-time, diverse partial shading patterns. It explores the various stages of the tracking process and the methodologies employed for optimization. While conventional methods have shown effectiveness, they often fall short in swiftly and accurately tracking maximum power points with minimal errors. To address this limitation, this research introduces a novel machine learning approach known as Adaptive Reinforcement Learning with Neural Network Architecture (ARLNNA) for MPPT. The results obtained from ARL-NNA are compared with existing algorithms using the same experimental data. Furthermore, the outcomes are validated through different factors and processing time measurements. The findings conclusively demonstrate the efficacy and superiority of the proposed algorithm in effectively tracking maximum power points in PV characteristic curves, providing a promising solution for optimizing solar energy generation in partial shading patterns. This study significantly impacts various realms of electrical engineering including power engineering, power electronics, industrial electronics, solid state electronics, energy technology and other related field of Engineering and Technology.
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Authors and Affiliations

M. Leelavathi
V. Suresh Kumar
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Abstract

Array jet impingement cooling is a significant technology of enhanced heat dissipation which is fit for high heat flux flow with large area. It is gradually applied to the cooling of electronic devices. However, The researches on the nozzle array mode and the uniformity of cooling surface still have deficiencies. Therefore, the influence of heat flux, inlet temperature, jet height, array mode and diversion structure on jet impingement cooling performance and temperature distribution uniformity is analyzed by means of numerical calculation. The results show that the heat transfer coefficient of jet impingement cooling increases linearly with the increment of heat flux and inlet temperature. With the increment of the ratio of jet height to nozzle diameter (H/d), the heat transfer coefficient increases first and then decreases, that is, there is an optimal H/d, which makes the heat transfer performance of the array jet impact cooling best. The temperature uniformity of array jet impact cooling is greatly affected by array mode. The improvement effect of nozzle array mode on temperature uniformity is ranked as sequential > staggered > shield > elliptical array. The overall temperature uniformity and heat transfer coefficient are increased by 5.88% and 7.29% compared with elliptical array. The heat transfer performance can be further improved by adding a flow channel to the in-line array layout, in which the heat transfer coefficient is increased by 6.53% and the overall temperature uniformity is increased by 1.45%.
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Authors and Affiliations

Nianyong Zhou
Youxin Zhou
Yingjie Zhao
Qingguo Bao
Guanghua Tang
Wenyu Lv
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Abstract

Energy storage systems (ESS) are indispensable in daily life and have two types that can offer high energy and high power density. Hybrid energy storage systems (HESS) are obtained by combining two or more energy storage units to benefit both types. Energy management systems (EMS) are essential in ensuring HESS's reliability, high performance, and efficiency. One of the most critical parameters for EMS is the battery state of health (SoH). Continuous monitoring of the SoH provides essential information regarding the system's status, detects unusual performance degradations and enables planned maintenance, prevents system failures, helps keep efficiency at a consistently high level, and helps ensure energy security by reducing downtime. The SoH parameter depends on parameters such as Depth of Discharge (DoD), charge and discharge rate (C-Rate), and temperature. Optimal values of these parameters directly affect the lifetime and operating performance of the battery. The proposed Adaptive Energy Management System (AEMS) uses the SoH parameter of the battery as the control input. It provides optimal control by dynamically updating the C-Rate and DoD parameters. In addition, the supercapacitor integrated into the system with filter-based power separation prevents deep discharge of the batteries. Under the proposed AEMS control, HESS has been observed to generate 6.31% more energy than a system relying solely on batteries. This beneficial relationship between supercapacitors and batteries efficiently managed by AEMS opens new possibilities for advanced energy management in applications ranging from electric vehicles to renewable energy storage systems.
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Authors and Affiliations

Gökhan YÜKSEK
Alkan ALKAYA

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