International Journal of Electrical Engineering and Computing
International Journal of Electrical Engineering and Computing (IJEEC) is a peer reviewed, open access scientific journal, published by the University of East Sarajevo, Faculty of Electrical Engineering, Bosnia and Herzegovina, and the publishing, design and marketing company Academic Mind, Belgrade, Serbia. The journal was created to promote the academic, professional community and research development visibility, spreading original and relevant articles in the wide range of subfields related to the electrical engineering and computer science. The papers reporting and containing original theoretical and/or practice oriented research and articles of interdisciplinary nature are all welcome.
University of East Sarajevo, Faculty of Electrical Engineering, East Sarajevo, Bosnia and Herzegovina
Copyright (c) 2026 International Journal of Electrical Engineering and Computing http://creativecommons.org/licenses/by-nc/4.0
Product Structure: A Data Model in the Context of Small and Medium-Sized Enterprises (SMEs)
In contemporary business environments—particularly amid accelerated digitalization—the capability for systematic management of information on products and production processes has become pivotal to achieving competitive advantage. For manufacturing small and medium-sized enterprises (SMEs), which operate under constraints in informational, material, and financial resources and under constant pressure to reduce costs and shorten delivery lead times, a clearly structured approach to product data management constitutes a key lever for achieving efficiency, quality, and market flexibility. In this context, the product structure and the corresponding data model are prerequisites for consistent, scalable, and interoperable information management throughout the entire product life cycle (from concept, development, and production to maintenance and recycling). This paper examines different approaches to defining product-structure data, including relational, object-oriented, and hybrid (multi-model) models. Different approaches to product data modeling for a manufacturing company are presented. The data model will include product structure data (engineering BOM), customer orders, and document management models. Finally, a brief overview of the advantages and disadvantages of each of the proposed solutions is given.
Edge-Aware Graph Neural Network Baselines for Protein Function Prediction on OGBN-Proteins
This paper presents an engineering-oriented study of edge-aware graph neural network baselines for protein function prediction on the OGBN-Proteins benchmark. The benchmark represents a large protein-protein interaction graph whose edges carry eight-dimensional association evidence and whose proteins have 112 functional labels. The study focuses on practical design choices that strongly affect reproducibility and deployment cost: construction of node features from edge evidence, use of scalar edge weights inside message passing, normalization under species-level distribution shift, and post-hoc decision calibration. We compare multilayer perceptrons, GraphSAGE, and GIN baselines in PyTorch Geometric, using mean, sum, and max edge-to-node aggregation, Batch Normalization, Layer Normalization, and a species-conditioned Layer Normalization variant. Results are reported over three seeds with ROC-AUC, micro-F1, calibrated micro-F1, expected calibration error, training time, memory use, and parameter count. Sum aggregation is consistently the strongest edge-to-node construction. GraphSAGE with sum-based features forms the best accuracy-cost trade-off, with Batch Normalization reaching the highest ROC-AUC and conditional Layer Normalization retaining stronger fixed-threshold behavior. Per-label temperature scaling and per-label thresholds substantially improve multi-label decision quality with negligible change in ROC-AUC, while light label-correlation smoothing yields small additional gains. The resulting protocol provides a compact, reproducible baseline for large edge-attributed biological graph settings
Enhancing MLOps for Educational Data Mining: A Comparative Study of Weka and KNIME in Model Lifecycle Management
This paper presents a comparative case study of Weka and KNIME for supporting Machine Learning Operations principles in Educational Data Mining. The goal of the study is to evaluate how two widely used data mining platforms support both analytical modeling and model lifecycle management in an educational data analysis context. The study uses the Ocisceni_Demografski_Podaci.csv dataset, containing approximately 15,000 demographic and education-related records from 42 municipalities. Both platforms were evaluated using the same preprocessing logic, classification, clustering, visualization, and lifecycle-management tasks. The analytical evaluation included Naive Bayes, k-nearest neighbors, K-Means, and Farthest First algorithms, while the lifecycle evaluation assessed workflow reproducibility, pipeline automation, deployment readiness, and integration extensibility using a five-level maturity rubric. The results show that Weka provides strong support for rapid algorithmic experimentation and model comparison, with Naive Bayes achieving 87.3% classification accuracy under the selected evaluation protocol. In contrast, KNIME provides stronger support for reproducible workflow construction, automation, integration with external systems, and operational deployment through server-based infrastructure. The findings indicate that neither platform is universally superior; rather, their usefulness depends on the phase of the machine learning lifecycle. The study concludes that educational institutions can benefit from a hybrid approach in which Weka is used for early-stage model exploration, while KNIME is used for workflow reconstruction, automation, reporting, and reproducible operationalization.
Machine Learning Operations, Educational Data Mining, Weka, KNIME, Workflow Reproducibility, Model Lifecycle Management, Data Pipeline Automation, Comparative Case Study
A new solution for the control system of the cross cutter in the board factory
Transversal cutters (flying shears) plants are used, among others, in continuous paper or cardboard production processes. In the technological processes of cardboard production, transverse cutters are located at their end, where the material (cardboard) in the form of a long strip is cut into shorter pieces (tabs). Continuity of the process requires that this cutting be done without stopping or slowing down the movement of the strip being cut. The basic technological requirements that should be met by the transverse cutter plant are: • Cutting the sheet to specified length; • High accuracy of the length of the cut pieces; • Quality cutting, without creasing, tearing, or crushing at the point of cutting. In the operation of transverse cutters, the strip being cut reaches the flying shears at the desired speed, which is determined by the speed of rotation of a pair of rollers called the press (press drive). Flying scissors consist of two blades placed perpendicular to the movement of the tape, the length of blades corresponds to the maximum width of the tape being cut (shear drive). The shears mechanism ensures that the blades are at the same moment opposite each other and cut off part of the web. To ensure proper cutting with high quality of the cut (tab edges) the peripheral speed of the blades must be equal to the speed of the web at the instant of cutting. The principle of operation of the cutter, whose control system will be described in this paper, is based on the variable speed of the motor (flying shears mechanism), which is directly coupled with the shears mechanism. The speed of the blades during one round (cutting cycle) changes, which is why the requirements for speed change dynamics, with the accuracy of speed and position synchronization in the period around the cutting, as well as the speed profile from one cutting to the next, are very demanding. Latest PLC from the Technology series, with integrated PROFInet and PROFIBUS DP communication protocols, was used to implement the control system of the transverse cutter. Press and knife drives are powered by high performance series frequency converters with integrated vector control algorithm for servo drive. PROFInet communication is also used to connect digital and analog input and output signals, via modules grouped into six groups. Frequency converters, whose communication with the PLC is realized by PROFIBUS DP communication, are used to power and control other drives (cart for changing the active unwinder - "rowing", decurling, elevator, forks and carts for change of pallets). PROFIBUS DP communication is also used for the integration of other "smart" devices (absolute encoders, web tension force measurement, etc.).
Application of DCT compression on images in IoT systems
Through the integration of sensors, software, and wireless connectivity into everyday activities, the Internet of Things (IoT) creates intelligent ecosystems whose applications are inevitably permeating all segments of life, including smart cities, agriculture, healthcare, and manufacturing. Such systems frequently utilize image and video data as information sources for machine learning and analysis, which are essential for anomaly detection, security surveillance, facial recognition, traffic optimization, and—in the case of this study—monitoring bee activity within a beehive. Given that digital images contain vast amounts of data transmitted through digital processing systems, it is vital to efficiently address the challenges of data transmission within resource-constrained IoT networks. The primary objective of the research presented in this paper is to demonstrate that the application of DCT (Discrete Cosine Transform) compression on images can significantly reduce data volume while simultaneously preserving the image quality required for further application. To evaluate the compression, DCT was applied to JPEG images obtained from smart beekeeping system cameras, utilizing the Python programming language with OpenCV and NumPy libraries. The images were sourced from publicly available datasets. The analysis involved applying DCT compression across six defined quality levels: 1, 5, 20, 50, 80, and 100, covering a range that allows for the assessment of the trade-off between compression ratio and image quality. As expected, the lowest quality factor yields the smallest file sizes but results in the lowest image quality. However, it is significant that even with a moderate factor of 20, substantial optimization is achieved — a fivefold reduction in data while maintaining satisfactory image quality for further processing. The optimal result is achieved at a factor of 80, which provides a twofold reduction in data and an output image where quality differences, compared to the original, are indiscernible to the naked eye.
DCT, quality factor, IoT, Python, image compression.
End-to-End Latency Decomposition in AI Web Applications: Rethinking Infrastructure in LLM-Based Systems
The increasing integration of artificial intelligence into web applications, particularly through large language models (LLMs), has fundamentally reshaped the performance characteristics of modern systems. Unlike traditional architectures, where latency is primarily determined by backend infrastructure, AI-driven applications operate as multi-stage pipelines involving orchestration logic, network communication, and external model inference. This paper introduces an end-to-end latency decomposition framework for analyzing performance in AI-powered web applications. A controlled experimental study is conducted using two production-equivalent implementations deployed in serverless and virtual private server (VPS) environments. The methodology distinguishes between full-stack execution, including LLM inference, and infrastructure-only scenarios, enabling precise isolation of latency contributions across infrastructure, application, and model layers. The results indicate that in full-stack scenarios, model-related latency dominates system performance, accounting for approximately 85% of total response time, thereby minimizing the impact of infrastructure differences. In contrast, infrastructure-only scenarios reveal significant performance variations between deployment environments. These findings challenge infrastructure-centric optimization approaches and demonstrate the need for system-level performance evaluation in LLM-based applications. The proposed framework provides a practical methodology for identifying performance bottlenecks and offers actionable insights for optimizing AI-driven web systems.
AI web applications; latency decomposition; large language models (LLM): serverless computing; virtual private server (VPS); end-to-end latency; performance evaluation; cloud computing; AI systems; benchmarking
Energy efficiency of air-to-water heat pumps at different operating modes in residential heating systems - case study in Bosnia and Herzegovina
This paper presents the possibilities of using air-water heat pumps for heating residential buildings. Description of the most important parts of the air-water heat pumps, as well as the basic guidelines for design and implementation, are shown in this paper. Practical example with heat pumps settings and measurements of capacity and Coefficient of Performance (COP) and Seasonal Coefficient of Performance (SCOP) are described in this paper. All research regarding air-to-water heat pumps of different operating modes was conducted at a real facility in Pazarić, location near Sarajevo, Bosnia and Herzegovina. In addition, the energy efficiency class was determined on the considered example of the heat pump. Based on these measurements, calculation and by comparison, important conclusions were obtained regarding the use of observed heat pump, control and maintenance of such systems in terms of increasing energy efficiency related to indoor comfort in the residential building.
Copyright (c) 2025 International Journal of Electrical Engineering and Computing http://creativecommons.org/licenses/by-nc/4.0
Analysis of the Effects of HVDC Technology on Regional Power System Security: Impact of the MONITA HVDC Interconnection
This paper provides an overview of High Voltage Direct Current (HVDC) technology, including its development, current status, key characteristics, limitations, and applications. It also presents power system security criteria, highlighting their importance for maintaining stability and reliable operation under all conditions. Special focus is given to the MONITA HVDC interconnection, examining its technical and operational features and evaluating its reliability under varying scenarios. The practical part of the study involves security analyses of the interconnection to assess its impact on the regional power system. Multiple operating conditions were considered, including normal operation, overload situations, and scenarios involving electricity import and export in the region. The objective of the research was to evaluate the behaviour of the MONITA HVDC interconnection under realistic operational conditions and its contribution to system stability and reliability. Detailed simulations were performed to assess its ability to maintain grid stability during sudden load variations, changes in power flows, and in cases of disconnections or failures of other network elements. The results demonstrate how the interconnection influences the regional power system’s operational security, identifying strengths and potential vulnerabilities. These findings provide valuable insights into the role of HVDC technology in enhancing the reliability and stability of modern power systems, and offer guidance for future planning, operation, and expansion of cross-border HVDC interconnections.
Artificial Intelligence Approaches to Battery Health Assessment: Opportunities, Challenges and Future Directions
Accurate estimation of the state of health and remaining useful life of lithium-ion batteries is essential for ensuring the reliability, safety and longevity of electric vehicles, stationary storage systems and portable electronics. Traditional approaches based on electrochemical diagnostics, equivalent circuit models and reduced-order physics-based methods provide valuable mechanistic insights but face significant limitations under dynamic real-world operating conditions. Recent advances in artificial intelligence have transformed battery diagnostics by enabling data-driven extraction of degradation-sensitive features from voltage, current and temperature measurements. Machine learning algorithms such as random forests and support vector regression demonstrate strong state of health estimation accuracy when combined with engineered features, while deep learning models, including convolutional neural networks, long short-term memory, convolution long short-term memory and attention-based architectures, achieve state-of-the-art performance by learning nonlinear temporal patterns directly from raw time-series data. Hybrid physics-informed neural networks further enhance interpretability and generalization by embedding electrochemical constraints into model architectures. In addition to supervised learning, reinforcement learning has emerged as a promising method for adaptive battery management, enabling real-time optimization of charging strategies, thermal control and power allocation to minimize degradation and extend battery lifetime. When integrated into digital twin frameworks, artificial intelligence models support continuous, real-time state of health/remaining useful life tracking and predictive maintenance across large battery fleets. Despite these advances, challenges remain in data availability, domain shift, model interpretability, computational constraints and the absence of standardized validation protocols.
Standards for procedures for measuring and calculating electromagnetic fields that may affect human
The paper presents the key features of the standards relating to measurements and calculations of electric and magnetic fields that may affect human health, with an emphasis on low-frequency radiation. Theoretical calculation methods and general knowledge related to levels and exposure to electromagnetic radiation in areas of the environment where human health may be affected are presented. Recommendations from European and international organizations for protection against non-ionizing radiation, as well as regulations at the national level, are given.
standard; electromagnetic field; electromagnetic radiation limits; law, regulations and recommendations; health protection;
Sensor-based IoT nodes under development for the Green Digital Transformation for measuring soil quality parameter
Green digitalization represents a modern concept that integrates digital technologies with the aim of enhancing sustainable development and promoting the rational use of natural resources. In the context of agriculture, it involves the application of intelligent IoT-based sensor systems and cloud infrastructure to optimize processes, reduce energy consumption, and protect the environment. Soil quality, as a fundamental resource of agricultural production, plays a crucial role in this process, as it directly affects the productivity and ecological stability of agroecosystems. In this paper, a cloud-oriented data acquisition system for soil quality monitoring has been developed, contributing to the goals of green digitalization through automation, energy efficiency, and the reduction of the need for manual sampling. The system is based on an Arduino microcontroller, an ESP8266 Node MCU Wi-Fi module, and a multifunctional RS485 4–20 mA soil sensor that measures temperature, moisture, pH value, and electrical conductivity. The collected data are automatically transferred to the Oracle Autonomous Database via REST Data Services (ORDS) and analyzed through the Oracle APEX application, providing remote access and real-time analysis. The proposed solution demonstrates how the integration of IoT technologies and cloud services can contribute to the development of green digital agriculture, enabling more precise resource management, reduced use of water and chemicals, and preservation of soil quality at the desired level. Such systems have significant potential for application in sustainable land management, environmental protection, and the achievement of green transition objectives.
Green digitalization, soil quality, IoT, cloud computing, sustainable agriculture, data acquisition system, Arduino
Short-term forecasting of PV power plants within the framework of green digitalization using Linear regression and Random forest models
This paper presents the application of various methods for predicting electricity generation in photovoltaic (PV) power plants using real experimental data obtained from the measurement of meteorological and operational parameters over a three-day period. The objective of the study was to develop a reliable and interpretable model for short-term prediction of PV system output power based on a limited set of available data. The research applied a linear regression model, multiple linear regression, and a Random Forest regression model. The models were trained using data from the first two days of measurement, while the third day was used for testing accuracy and verifying model performance. The input parameters included solar radiation, module temperature, wind speed, and ambient temperature, while the target variable was the measured output power of the power plant expressed in megawatts. The results show that both linear and multivariable linear regression achieved a high level of agreement between measured and predicted values, with multiple linear regression reaching an R² of approximately 0.97, indicating that it explains about 97% of the variations in output power. However, the Random Forest model demonstrated superior performance, achieving an R² of about 0.975 on the test set, due to its ability to model complex and nonlinear relationships between meteorological parameters and power generation. The analysis confirms that even from a limited three-day dataset, it is possible to build a stable, robust, and accurate model for short-term PV power output prediction.
Keywords- photovoltaic power plants, power generation forecasting, linear regression, multiple linear regression, Random Forest, short-term prediction, meteorological parameters.
Research for Аpplication on Plasma Treatment in Mechatronics
The report provides a historical overview of the topic, examines the physical basis of plasma production, thermal emission and treatment area, types of plasma treatments and the state of technology, types, development and potential of equipment and its application in practice. The advantages and disadvantages are analyzed in Mechatronics.
Improving the concept of object-oriented programming in modern programming languages
In software development object-oriented programming (OOP) stands as the principal method because it delivers modular and scalable and reusable code. The traditional OOP implementation models encounter substantial difficulties when managing intricate software projects which resulted in the creation of modern programming languages featuring superior OOP characteristics. This research investigates the modern developments in OOP through assessment of Kotlin together with Rust and Swift along with Scala. The recent improvement packages include trait-based programming along with pattern matching and immutability and Hybrid paradigm unification. Evaluation of software updates takes place at an operational level where efficiency boosts alongside improved robustness and enhanced adaptability receive assessment. The component-based programming model of Trait-based programming serves as a deep inheritance structure replacement to lower code maintenance issues. Program safety increases when using Pattern matching combined with type enforcement and the tool simplifies if-then decisions and enables concurrent execution and debugging operations. The research presents an in-depth analysis of new programming language technologies which address classic OOP limitations through higher software development competency improvements.
object-oriented programming, trait-based programming, pattern matching, immutability, functional programming, software engineering
IoT System for Indoor Air Quality Monitoring in Accordance with European and International Standards
The Internet of Things (IoT) has facilitated the creation of affordable and dependable solutions for real-time environmental monitoring. This paper discusses the design and evaluation of a smart indoor air quality monitoring system that combines an M701 multifunctional air quality sensor with an ESP32 microcontroller. The system measures carbon dioxide, particulate matter (PM₂.₅ and PM₁₀), formaldehyde, total volatile organic compounds, temperature, and relative humidity. Data from the sensor module are securely transmitted to the server using the Message Queuing Telemetry Transport (MQTT) protocol over Transport Layer Security (TLS), ensuring data integrity and confidentiality. Measurements were taken in a 30 m² office space under stable operating conditions. The results showed that most values remained within the limits recommended by international and European guidelines, although occasional exceedances were noted for formaldehyde and total volatile organic compounds. The system exhibited high reliability, stability, and scalability, making it suitable for integration into heating, ventilation, and air conditioning systems, as well as for implementing predictive analytics for proactive air quality management.
Operational planning and security management in modern power systems under regional coordination conditions
Deregulation of the electricity market has brought not only numerous benefits but also significant challenges in the domain of operational planning and management of the power system. The increase in cross-border trade required improved coordination and data exchange among Transmission System Operators (TSOs). In response, Regional Security Coordinators (RSCs) were established to centralise the coordination of TSOs' operations and provide forecasts of the power system state over short- and medium-term time intervals. This paper aims to provide insight into the key aspects of RSC operations, emphasizing their significant role in planning, coordinating, and implementing security strategies at the regional and Pan-European level. Although the data presented in this paper is mainly theoretical due to the confidentiality of information managed by RSCs, this paper nevertheless serves as a valuable resource given that the concept of RSCs has been insufficiently addressed in professional literature and scientific studies.
Copyright (c) 2025 International Journal of Electrical Engineering and Computing
Residue Number System-Based Hash Function: Design and Cryptographic Analysis
In this paper, we propose a novel cryptographic hash function based on the Residue Number System (RNS). The design leverages the inherent parallelism of RNS to enhance computational efficiency and resilience against certain classes of attacks. Through modular processing across independent residues, the function achieves notable improvements in hardware acceleration capabilities, making it particularly suitable for resource-constrained environments. To assess the robustness of the proposed function, we conduct a series of tests including the distribution of Hamming weights, bit balance evaluation, and avalanche effect analysis. The results demonstrate that the hash function produces uniformly distributed outputs, exhibits strong avalanche characteristics, and maintains high resistance to input structure correlation. These properties confirm the potential of the proposed RNS-based approach for secure and efficient hashing in modern cryptographic applications.
Two and Three Dimensional Support Vector Algorithm for Students’ Classification
In an era where technology with its development is constantly reshaping the field of professional practice, it is evident that educating engineers nowadays is a challenging task. This is supported by the increasingly popular teaching methods in which traditional learning methods are combined with modern technologies, all with the aim of greater motivation for learning and work among the students. The subject of research in this paper is specifically one teaching method, adapted for teaching at technical faculties, which uses machine learning methods (support vector method) in its background. The introduced method recognizes the importance of assessing students' abilities and knowledge because a good classification of students allows the teacher to plan the lesson more easily and choose the approach they believe will give the best results.
Population Genetic Analysis of Allele Frequencies for Hereditary Thrombophilia in Pregnant Women Using Artificial Intelligence
Thrombophilia is an inherited condition characterized by an increased tendency for blood clot formation, affecting approximately 8-11% of the European population. It is typically inherited in an autosomal dominant manner and includes approximately 10 different subtypes, classified based on genetic factors. This condition often leads to complications in pregnant women, including spontaneous miscarriage, fetal deformities, and an increased risk of heart attack, stroke, pulmonary embolism, and deep vein thrombosis. The goal of this study was to conduct a detailed genetic analysis of thrombophilia using population genetics methods (Hardy–Weinberg equilibrium, Shannon index, genotype frequencies) and to develop an AI-based predictive model for high-risk genotypes, evaluated via confusion matrices. The research focused on assessing allele frequencies, genetic diversity, and deviations from Hardy-Weinberg equilibrium in a cohort of 2,760 pregnant women to enhance the understanding of the genetic basis of this condition. Our analysis revealed significant correlations between coagulation factor genes and identified distinctive patterns of genetic diversity across 12 thrombophilia-associated markers. The results provide key insights into genetic variations and their potential implications for pregnancy complications. This study opens new perspectives for improving early detection and more effective risk management of thrombophilia during pregnancy.
Population Genetics; Allele Frequencies; Thrombophilia; Genotypic frequency; Pregnancy; Shannon index; Hardy-Weinberg Equilibrium (HWE); Artificial intelligence;
CodeCrafter – Efficient Code Generator for Modern Single-page Web Applications
Web applications and REST API based applications can be seen as a most common type of application today and their number is constantly increasing. Due to the pressure to produce more code and be faster at producing it, developers have been using various programming languages, libraries and frameworks suitable for such applications. Two of the most popular technologies are Spring on the back-end, and React on the front-end. While each technology provides a satisfiable functionality, the amount of code that needs to be produced is non-trivial, especially in cases where the application has to provide even basic security and auditing functionalities. One solution for this issue is use of code generator tools that produce both back- and front-end code from a model. While a wide variety of generators exist, none of the analyzed ones fulfill all the requirements of a modern web application. In this paper, we present CodeCrafter, a web-based code generation application that produces Spring and React code based on relational data model in DDL or JSON format, and provides developers with simple and efficient tool to generate fully functional foundational code. We compare it to existing code generation tools and measure its performance as a function of number of tables in the database. We show that CodeCrafter produces code in very short amount of time even for very complex databases, while providing features and functionalities not present in other analyzed tools. We also give a short overview of possible use in conjunction with LLM based coding tools.
Optimization and Construction of a Deep Learning Model for Breast Cancer Segmentation
The research focuses on optimizing and developing a breast cancer segmentation model utilizing multi-GPU arrays to maximize hardware resource utilization. By exploring various architectural strategies, the study aims to enhance resource allocation efficiency despite existing limitations. Detailed evaluations using mammography datasets have demonstrated significant improvements in tumor detection capabilities. This technology holds the potential to revolutionize breast cancer detection, a critical advancement given the global impact of the disease. Training data analysis confirms the scalability of these results across diverse hardware configurations, ensuring high efficiency and reliability. The study employs modern architectures, contributing valuable insights to the field of breast cancer segmentation and advancing medical imaging technologies. The development of efficient and clinically viable solutions is imperative in contemporary medical image analysis. While state-of-the-art deep learning architectures offer impressive capabilities, their substantial computational demands pose barriers to widespread clinical adoption. This research addresses the need for solutions that efficiently process large datasets while maintaining diagnostic accuracy, facilitating integration into clinical workflows and reducing operational costs.
machine learning; AI; image segmentation
Copyright (c) 2024 International Journal of Electrical Engineering and Computing
Lightning protection of the 110 kV substation at the wind park Ivan Sedlo against transients caused by direct lightning strikes into connected overhead line
This paper deals with the lightning protection of 110 kV substation at the wind park Ivan Sedlo against transients caused by direct lightning strikes into connected 110 kV overhead line. Lightning protection level (LPL) I as per standard IEC 62305-1 is selected for this substation to reduce probability of equipment failure to low level. Calculations are done in the EMTP-ATP software. Three critical scenarios are analysed: shielding failure at the connected 110 kV overhead line, lightning strike to the top of first tower in front of the substation and lightning strike to the top of gantry tower at the entrance to the substation. It is presented that LPL I for analysed configuration of substation can be achieved by installing two sets of surge arresters. First set of surge arresters is installed in the transmission line bay, while second set of surge arresters is installed in the transformer bay. Transferred lightning transients through the power transformer are also calculated. It is presented that one additional set of surge arresters is necessary at the 30 kV side of the power transformer, to protected it against transferred lightning transients. Energy duty of all three sets of surge arresters is calculated. At the 110 kV voltage level energy stress of surge arresters is significant and strongly depends on the substation grounding resistance value. Estimated energy stress of set of surge arresters at the 30 kV voltage level is very low.
lightning protection; overhead line; shielding failure; substation; surge arrester; wind park
MQTT Protocol in Smart Home Environments: Principles of Operation and Application
Internet of Things as a revolutionary concept in technology of modern days has led to the popularization of “smart” things. It introduces low-cost and reliable solutions to provide independent operations of devices and machines. Smart home systems have become one of the most important implementations as they significantly improve the quality of people’s lives. Since communication plays a key role in these systems, this article provides insights into Message Queuing Telemetry Transport protocol application in smart home systems. Although it is not the only protocol used, MQTT stands out in terms of reliability, efficiency, and simplicity. The article provides major MQTT characteristics, advantages and its application in a particular smart home system for ambient parameters monitoring and control. The system integrates NodeMCU based sensor node with mobile and web applications via MQTT protocol. Results of the performance analysis of MQTT use in this system are given. Tests have shown great connection establishment speed, a high percentage of successfully sent messages, and a large number of messages sent per minute. Thanks to the flexibility of the MQTT protocol, the system is easy to upgrade and expand.
A System for Remotely Monitoring and Controlling Temperature within an Egg Artificial Incubator
Abstract— According to a report by the Food and Agriculture Organization of the United Nations, poultry meat is the leading contributor to global growth in total meat production. This growth is facilitated by the short production cycle of poultry, advancements in genetics, animal healthcare, feeding methods, breeding techniques, and improved environmental conditions, enabling producers to adapt quickly to market demands. Proper hatching practices are critical to maximizing the number of healthy chicks and minimizing their mortality rate. There are two primary methods of hatching: natural incubation, where hens incubate eggs by sitting on them, and artificial incubation, which uses incubators. In artificial incubation, monitoring and controlling key environmental factors, particularly temperature and humidity, are essential. Maintaining the incubator's temperature within an optimal range is crucial, with literature recommending a range of 37°C to 38°C and an ideal temperature of 37.8°C. This paper introduces a system for remotely monitoring and controlling temperature within an artificial incubator. The system integrates hardware and software based on the Internet of Things (IoT) framework. For this purpose, a proportional-integral-derivative (PID) controller is designed to maintain the desired temperature, with temperature data stored in a database and accessible online. The proposed system offers a cost-effective yet highly efficient solution, primarily designed for small-scale hatcheries. With modifications, it can be adapted for broader agricultural applications, including greenhouses, silos, and stables. By contributing to digital agriculture, the system supports sustainable farming practices, which are essential for the future of agriculture.