IJEEC - INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTING

Standards for procedures for measuring and calculating electromagnetic fields that may affect human

Miljan Vasković — 2026-02-25

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.

Analysis of the Effects of HVDC Technology on Regional Power System Security: Impact of the MONITA HVDC Interconnection

Anđela Krljaš — 2026-02-25

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.

IoT System for Indoor Air Quality Monitoring in Accordance with European and International Standards

Milica Ivković — 2026-02-25

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.

Research for Аpplication on Plasma Treatment in Mechatronics

Stefan Kartunov — 2026-02-25

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.

Sensor-based IoT nodes under development for the Green Digital Transformation for measuring soil quality parameter

Božidar Popović — 2026-02-25

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.

Artificial Intelligence Approaches to Battery Health Assessment: Opportunities, Challenges and Future Directions

Nataša Popović — 2026-02-25

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. Future research will focus on physics-informed hybrid artificial intelligence, transfer learning for cross-chemistry generalization, federated learning for privacy-preserving fleet deployment and standardized benchmarking frameworks. Together, these developments signal the emergence of next-generation intelligent battery management systems that combine accurate health estimation with adaptive, degradation-aware control.

Short-term forecasting of PV power plants within the framework of green digitalization using Linear regression and Random forest models

Božidar Popović — 2026-02-25

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. The Random Forest model proved to be the most reliable
solution for this type of task, while multiple linear regression provided a simple and efficient baseline approximation suitable for rapid implementation in real-time solar energy monitoring and management systems.

Improving the concept of object-oriented programming in modern programming languages

Jasna Hamzabegović — 2026-02-25

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. Trait-based programming replaces deep inheritance structures, reducing code maintenance complexity. 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.