AEEE. 2025, vol. 23

Permanent URI for this collectionhttp://hdl.handle.net/10084/158014

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Now showing 1 - 17 out of 17 results

A Novel Method for Multiple Sound Sources Localization with Low Complexity
(Vysoká škola báňská - Technická univerzita Ostrava, 2025) Hieu, Nguyen Trung; Yamada, Kou
Sound source localization is essential in many areas such as robotics interaction, teleconferenc- ing, sound extraction and recognition, noise cancellation in vehicles, object location detection, assessment of noise pollution in living spaces, and search and rescue. Inter- action in natural settings requires the detection of differ- ent sources of sounds from the environment. Accurately detecting and differentiating incoming sound directions always attracts attention and has been researched us- ing various methods. However, most of these methods still require complex algorithms or large amounts of calculations, which are accompanied by the cost of hard- ware and system resources. In this paper, we present a novel method and metrics for estimating the direction of multiple sound sources based on a combination of beamforming, time difference of arrival (TDOA), and frequency sparsity. Our new proposals are well-suited for deployment on resource-limited devices, offering re- duced processing complexity, short computation time, and real-time response.
Bearing Fault Diagnosis Based on Convolutional Neural Network using Estimated Motor Current Signals and their Spectral Portrait
(Vysoká škola báňská - Technická univerzita Ostrava, 2025) Dang, Huu Hai; Bui, Ngoc-My; Hoang, Van-Phuc; Bui, Quy Thang; Doan, Van Sang
Induction motor bearing fault diagnosis stands as a crucial aspect of rotating machinery maintenance. Numerous studies have delved into employing current signals and machine learning methods for this purpose. However, the effectiveness of these approaches relied heavily on manually selecting features for training. Moreover, traditional machine learning techniques struggle with large volumes of computational data. To address these limitations, researchers have turned to deep learning architectures such as Convolutional Neural Networks, ResNet, and AlexNet, either individually or in combination with traditional machine learning methods, for bearing fault diagnosis. Published convolutional neural network-based works usually use basic CNN networks. The experimental data are time or frequency domain data, and the fault classification accuracy is high only with noise-free signals. This paper proposes a novel approach aimed to enhance the accuracy of bearing fault identification by leveraging a CNN model trained on both the estimated motor current signals and their corresponding Fast Fourier Transform values. Comparative analysis against existing methodologies including machine learning and single-input convolutional neural networks or multiinput convolutional neural networks demonstrates that the proposed method achieves impressive results. The bearing fault accuracy reaches up to 99.88% for noisefree signals and 99.14% for signals with added noise at a Signal-to-Noise Ratio of -10 dB.
A Design of Smart Feeding System for Dairy Cows Farm
(Vysoká škola báňská - Technická univerzita Ostrava, 2025) Tran, Nhat Minh; Tran, Thang Viet; Nguyen, Ngo Minh Tri; Ho, Minh Nhut; Nguyen, Chi-Ngon
In this study, we present the design of an automatic food supply system for dairy cows using an expert calculation formula based on collected data, including temperature and humidity of the barns, cow movement, daily milk production, the breeds and ages of the herd. When the expert system calculates the amount of the total mixed ration (TMR) feed to provide to the cows, it will transfer the TMR value to the proposed system to provide the exact amount of the daily feed. The designed feeding system performs 3 tasks: 1) Accurately quantify the amount of the TMR feed ingredients according to the formula including chopped grass and fine feed portions; 2) Mixing the measured ingredients during a preset time from the system; 3) Transporting the feed after mixing to the feeding trough for dairy cows and then automatically retrieving the leftover feed. The proposed system was tested at the Tan Tai Loc dairy cow farm, Soc Trang City, Vietnam. The results show that the automatic weighing system can reach ±0.5% accuracy, and the proposed feeding system can minimize feed loss and eliminate overfeeding to promote good cow health and performance.
Zebra Optimization Algorithm for Power Conditioner with Fractional order PID Controller for Power Quality Improvement in Photovoltaic Energy System
(Vysoká škola báňská - Technická univerzita Ostrava, 2025) Maklad, Ahmed M; Morsy, Gamal A.; Khattab, Heba A; Amer, Ragab A.
The growing use of advanced equipment in modern systems, such as electronic devices and drives, has led to a decline in power quality (PQ), causing malfunctions in sensitive loads. Additionally, the integration of renewable energy sources into the power grid significantly impacts the PQ of the electrical system. To address these effects on voltage stability and harmonic distortion, the unified power flow controller (UPFC) series compensator has proven to be a highly effective solution. This study focuses on using the UPFC to mitigate PQ issues related to renewables, including voltage sag, swell, harmonics, and fault conditions. The UPFC is controlled by a fractional order proportional integral derivative (FOPID) controller, which uses the improved zebra optimization algorithm (ZOA) to determine optimal gain values under various PQ scenarios. Furthermore, three comparative assessments of different optimization approaches are conducted to achieve the desired performance and power of the proposed UPFC. The results showed that the proposed ZOA approach compared with WOA and PSO yielded the shortest computing time of 173.554, 257.544, and 382.405 seconds and achieved an objective function value of 2.371, 2.387, and 2.398, respectively. The effectiveness of the proposed strategy is validated using the MATLAB/Simulink platform, with results showing significant improvements in voltage stability and harmonic reduction.
Performance Comparisons between Conventional and Hairpin Winding Configurations of V-Shaped IPMSM
(Vysoká škola báňská - Technická univerzita Ostrava, 2025) Quoc, Vuong Dang; Minh, Dinh Bui; Duc, Hung Bui; Chi, Phi Do
Interior permanent magnet synchronous motors (IPMSMs) have been used extensively in the transportation, industrial, medical, and military fields recently because of their many benefits, including high power density, high torque, and high operational economy. However, current studies have mainly focused on using traditional windings for these motors, neglecting the potential improvements in motor performance that could be achieved with different winding types. Therefore, it is crucial to evaluate the performance of these motors when using different winding structures. This paper presents a combination of analytical technique and finite element method to compare the electromagnetic parameters (back electromotive force (EMF), output power and torque, and temperature rise) of IPMSMs with conventional and hairpin winding configurations. The validated method is then applied to a practical V-shape IPMSM with conventional and hairpin winding configurations.
Smart Military Logistics Based on Internet of Things and Energy Harvesting
(Vysoká škola báňská - Technická univerzita Ostrava, 2025) Malik, Mandee; Kothari, Ashwin; Pandhare, Rashmi
Logistics is a challenge in military as things are fluid and on the move constantly 24 X 7. Managing inventory for weapon systems, vehicle parts, ammunition, food items, utensils, clothing and other day-today necessary items is cumbersome and needs a professional solution incorporating the geographical challenges faced by military. Supply chain in military is done manually and thus prone to errors due to poor inventory management and manual documentation. This paper brings a green solution to this problem with minimum resource requirement and maximum coverage, also keeping in mind the remote area challenges such as power and weather conditions. Backscatter radios communicate by means of reflections without any batteries; rather they generate energy from ambient electromagnetic waves through energy harvesting. Multistatic scatter network uses carrier emitters and readers to gather information from hundreds of tags placed on different set of items spread over a large area. This data generated by reader from tags in a warehouse can automate the inventory management and lead to smart logistics system for military cargo facilities. This paper proposes a multi-static topology for large warehouses with non-linear energy harvesting for effective coverage of cargo facility. Paper also gives an insight on information and energy outage in small-scale fading scenarios. Comparison with linear energy harvesting and mono-static model is also carried out and substantiated by simulations.
Enhancing IEEE 802.11ah Networks: A Spatial Multi-Channel MAC Protocol
(Vysoká škola báňská - Technická univerzita Ostrava, 2025) Dang, Duc Ngoc Minh
IEEE 802.11ah is designed to enhance IoT networks by supporting numerous stations, extending coverage range, reducing power consumption, and operating within the sub-1 GHz band. The Restricted Access Window is introduced to mitigate collisions and improve network throughput when multiple stations contend for the channel simultaneously. While the PHY layer in IEEE 802.11ah supports multiple channels with different bandwidths, the MAC layer only supports a single channel for communication between the Access Point (AP) and stations. However, network performance can be improved by enabling stations at different locations to exchange data packets on different channels concurrently. This paper proposes a Spatial Multi-channel MAC (SM-MAC) protocol for IEEE 802.11ah, which divides the AP’s coverage area into sectors, each assigned a dedicated channel. Each sector is served by two Forwarders responsible for relaying data packets between stations and the AP. The performance of the SM-MAC protocol is evaluated against the IEEE 802.11ah MAC protocol in terms of packet delivery ratio, throughput, and energy efficiency.
Modeling of Asynchronous Units of a Powerful Pumping Station in the Pressure Stabilization Mode
(Vysoká škola báňská - Technická univerzita Ostrava, 2025) Lysiak, Vladyslav
The paper is devoted to developing a mathematical model of a powerful controlled pumping station with automatic pressure stabilization. The work aims to increase the efficiency of the liquid transportation process through main and large distribution pipelines. The proposed model with a comparable level of detail describes the electromechanical and hydraulic subsystems as a single entity. The equations of the hydraulic subsystem are formed based on the principle of electrohydrodynamic analogy and reflect the physical processes in its components. The parameters of the centrifugal pump model were calculated based on the geometry and dimensions of its internal elements, taking into account the influence of the physical properties of the working fluid. It makes it possible to conduct a comprehensive study of such objects without physical impact on them, taking into account the mutual dependence of both subsystems and changes in the parameters of the elements of the hydraulic subsystem. The paper offers directions for use and ways to improve the functionality of the developed model.
Multi-Power Beacon Empowered Secure In Iot Networks: Secrecy Outage Probability Analysis
(Vysoká škola báňská - Technická univerzita Ostrava, 2025) Hung, Tran Cong; Nguyen, Quang Sang; Minh, Bui Vu; Nguyen, Thu-Quyen Thi; Nguyen, Ngoc-Long
This paper investigates the physical-layer secure performance of a multi-power beacon-empowered wirelessly powered communication system in the surveillance of an external eavesdropper. Specifically, a limited-energy source harvests energy from multiple dedicated power beacons in the first time slot and reuses it to perform data transmission in the second time slot. However, its communication with the destination is wiretapped by a potentially idle untrusted user. Thus, to evaluate the secure performance of the considered system, we have derived closed-form expressions for the secrecy outage probability (SOP) metric in terms of both exact and asymptotic aspects. Through these formulas, we then provide a series of numerical discussions on how to configure the number setting of the power beacon, the energy harvesting parameters as well as scheduling time to improve the SOP performance, where the Monte-Carlo simulation method is used to verify the developed mathematical framework.
How Generative AI and the Intelligent Industrial Internet of Things Complement Each other
(Vysoká škola báňská - Technická univerzita Ostrava, 2025) Sikora, Axel
Generative modeling is an artificial intelligence (AI) technique to generate synthetic artifacts from analyzing training examples and from learning their patterns and distribution. Generative AI (GenAI) uses generative modeling and advances in Deep Learning to produce diverse content at scale by utilizing existing data. Whereas traditionally GenAI is mostly using media contents, such as text, graphics, audio, and video, it can additionally be used also for data from the (Industrial) Internet of Things. This article provides a systematic overview on the manifold different practical opportunities and challenges GenAI brings for the IIoT. It also presents selected examples from the author’s research with his teams. In doing so, it covers the relevance of GenAI for the complete lifecycle of IIoT, from design and development, over testing to deployment. This paper summarizes a keynote presentation from the 13th International Conference on Green and Human Information Technology (ICGHIT) in January 2025 held in Nha Trang, Vietnam.
The Impacts of Electric Charging Stations on Distribution Power Grids under Different Simulations using Jellyfish Swarm Algorithm
(Vysoká škola báňská - Technická univerzita Ostrava, 2025) Tran, Dao Trong; Duong, Minh Phuc
This research presents different implementations for placing electric charging stations (ECSs) in distribution power networks (DPNs) to achieve the best total active power loss (TAPL). Solar generators (SGs) are also used to alleviate the adverse effects resulting from the presence of ECSs in the networks in terms of power loss and voltage profile. Artificial hummingbird algorithm (AHA), Jellyfish swarm algorithms (JS), and Northern goshawk optimization (NGO) are executed to determine the best placement of ECSs and SGs in an IEEE 33-node network for reaching a minimum loss and satisfying all the related constraints. There are four cases conducted in the whole research. In the first case, JS outperforms both AHA and NGO by providing the highest stability throughout all the trial runs and fastest convergence speed to the optimal solution in the best runs. Besides, the quantitative comparison also consolidates the robustness and reliability of JS compared to others. Based on the surprising performance, JS is continuously reapplied to solve another three cases of the considered problem. Through those three cases with the application of JS, the TAPL values of four scenarios with different numbers of ECSs are evaluated. Specifically, the results achieved by JS indicate that the higher number of ECSs leads to a higher value of TAPL and a higher voltage drop. On the other hand, the simultaneous placement of SGs and ECSs can result in smaller fluctuations of the voltage profile and smaller TAPL. Thus, the optimization placement of ECSs and SGs is crucial to DPNs for economic and technical purposes.
Comparative Evaluation of Maximum Power Point Algorithms in Photovoltaic Systems for Renewable Energy Utilization
(Vysoká škola báňská - Technická univerzita Ostrava, 2025) Çakir, Mervenur Kutlu; Kaysal, Ahmet; Oĝuz, Yüksel
The irregular generation patterns of renewable energy systems lead to undesirable fluctuations in power grids. Integrating energy storage facilities into renewable energy systems is proposed as a solution to this issue. In this study, a photovoltaic energy system with energy storage is designed, and the effects of deterministic and stochastic optimisation-based algorithms on maximum power point tracking are analysed to ensure high-efficiency operation. In the designed system, maximum power point tracking of the photovoltaic system is achieved using the conventional Perturb and Observe, Incremental Conductance, Fuzzy Logic-Based Perturb and Observe, and Particle Swarm Optimization. The algorithms are extensively compared based on performance metrics such as rise time, settling time, and overshoot rate. The Fuzzy Logic-Based Perturb and Observe algorithm exhibits the best performance, with a rise time of 14.28 milliseconds and a settling time of 51.6 milliseconds, achieving the highest efficiency with a battery state of charge level of 69.97%. Detailed simulation analyses conducted in the Matlab/Simulink environment reveal that the fuzzy logicbased method provides faster and more stable results than other methods. Furthermore, a 24-hour real solar irradiance dataset is utilised to test the model under realistic environmental conditions, allowing for a more reliable evaluation of the performance of our storageintegrated photovoltaic.
Joint Power Control and Relay Selection with Short Packet Communications under Co-channel Interference
(Vysoká škola báňská - Technická univerzita Ostrava, 2025) Anh, Uyen-Vu Le; Nguyen, Xuan-Phuong; Nguyen, Tien-Tung
n this paper, a cooperative system where one multiple antenna transmitter communicates with one single antenna receiver with assistance of multiple relay nodes is considered. Under this system setting, with co-channel interference affecting on the relays, we evaluate the system in short packet communication (SPC). Relied on SPC metric, average block error rate (BLER) of the receiver corresponding to given relay is calculated. Next, due to multiple relay, we formulate a problem which joint power allocation and relay selection to minimize the BLER. To address the problem, we divide it into two sub-problems, which are power allocation and relay selection problems. The proposed solution’s effectiveness is validated through simulation and analysis results, which demonstrate its superior performance over benchmark methods.
Fractional Order and Proportional Resonant Controller Based Virtual Power Plant to Enhance the Time Response in Distribution Network
(Vysoká škola báňská - Technická univerzita Ostrava, 2025) Tamilselven, Kesavan; Manvel, Murugesan; VJ, Vijayalakshmi; Palani, Lakshmanan
The concept of Virtual Power Plant (VPP) emerges as a pivotal advancement in power systems engineering, aiming to enhance the integration of renewable energy sources into the power market. This research delves into the challenges faced by Distributed Energy Resources (DERs), such as power losses, voltage variations, and revenue losses in the distribution network, hindering the effective participation of small-scale renewable energy sources in the power market. Furthermore, the research addresses the challenge of poor dynamic response in VPPs attributed to dynamic loads and DER characteristics. To mitigate this, a Proportional Resonant (PR) controller is proposed to enhance the dynamic performance of the VPP. Comparative analysis with the conventional Fractional Order Proportional Integral Derivative (FOPID) controller reveals the superiority of the PR controller. The PR-controlled VPP exhibits improved dynamic performance with a lower settling time of 0.49 seconds and a reduced steady-state error of 2.78 compared to the FOPID controller. The investigations extend to the comparison of voltage, real power, and reactive power between the FOPID and PR controllers. The results underscore the superior response of the PR-controlled VPP, showcasing its efficacy in achieving faster responses and minimizing spikes in voltage and power variables.
Enabling D2D Transmission Mode of Reconfigurable Intelligent Surfaces Aided in Wireless NOMA System
(Vysoká škola báňská - Technická univerzita Ostrava, 2025) Le, Viet-Dung; Nguyen, Hong-Nhu; Nguyen, Sang Quang; Bui, Trong-Tu; Hien, Dinh Cong; Kim, Byung Seo
Reconfigurable Intelligent Surfaces (RISs) are emerging as a promising alternative for future wireless networks. This study investigates deviceto-device (D2D) communication systems using nonorthogonal multiple access (NOMA) and RISs between user devices and base stations to enhance the performance of the proposed system. The closed-form and approximate-form expressions of outage probability (OP) were demonstrated with the impact of imperfect successful interference cancelation (ipSIC) to give more key insight into our work. Finally, the Monte Carlo simulation is adopted to clarify the accuracy of the analysis of mathematical expressions.
Control and Identification of Single Machine Infinite Bus with Neural Network for System Stability
(Vysoká škola báňská - Technická univerzita Ostrava, 2025) Alhalim, Shaimaa Shukri Abd.; Bahloul, Wissem; Chtourou, Mohamed; Derbel, Nabil
This paper deals with implementing artificial neural networks for the identification and control Investigating the stability and stabilization of a single machine connected to an infinite bus through a transmission line (SMIB) system. Artificial Neural Network (ANN) employs a multi-layer feedforward network trained using the Backpropagation (BP) algorithm by simulations using MATLAB/Simulink. Weight coefficients of the ANN are determined using the LevenbergMarquardt algorithm. The proposed approach uses two types of neural networks: neural controller and neural identification, neural network control is a single device on an infinite bus instead of the PID-PSS controller, to improve the performance of the SMIB system, and neural identification to emulate the characteristics of the single machine infinite bus (SMIB) system These neural networks model system dynamics and nonlinear for selection and control purposes. The primary objective is to develop a neuronal identification model that accurately equals the characteristics of the single machine infinite bus (SMIB) system and a neuro-controller is implemented to replace traditional controllers such as Power System Stabilizers (PSS) and Automatic Voltage Regulators (AVR). Simulations are performed to examine the system under various conditions, evaluating rotor speed deviation, stator voltage, and rotor angle delta.
Optimization of Wind Farm Layout for Maximum Energy Production by Stochastic Fractal Search
(Vysoká škola báňská - Technická univerzita Ostrava, 2025) Nguyen, Khoa Dang; Tran, Tinh Trung; Vo, Dieu Ngoc
The wind power plant designs are different from the design of other conventional power plants such as hydropower plants, thermal power plants, and nuclear power plants because the input fuel of these types of power plants is controllable. Wind power plants depend on the speed of wind energy. Therefore, the problem of optimizing the location of turbines in a wind farm to achieve maximum annual energy output (AEP) is of great interest. In this paper, the Stochastic Fractal Search (SFS) algorithm is proposed to optimize the arrangement of turbines in the wind farm to minimize the wake effect so that the wind farm achieves the maximum generating capacity and the highest power factor (CF). SFS represents a significant advancement in optimization techniques, offering robust, adaptable, and efficient solutions to complex problems like wind farm layout optimization. Its innovative use of fractional dynamics and stochastic processes distinguishes it from traditional methods, providing superior performance in many scenarios. The proposed method was tested on a standard case with three types of turbines with different capacities of 850kW, 1000kW, and 1500kW to confirm the suitability of the algorithm and select the most appropriate turbine type. The results of AEP and wake loss calculated by the SFS algorithm were superior compared to those obtained by the PSO algorithm for these three turbine types. The turbine with the highest CF will be selected for application in the wind farm. Therefore, the proposed SFS algorithm can be a potential method to deal with the problem of optimization of wind farm layout.

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