Faculty of Electrical Engineering and Information Technologies
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Item type:Publication, 2024 11th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN)(IEEE, 2024-06-03)M. Srbinovska, S. Pechkova, A. Pechkov, M. Celeska Krstevska, A. Krkoleva Mateska, P. Dimovski, V. AndovaThe study delves into the realm of air quality forecasting, employing the LASSO (Least Absolute Shrinkage and Selection Operator) modeling technique for enhanced predictive accuracy. Utilizing a diverse dataset encompassing meteorological parameters, pollutant concentrations, and other relevant factors, the research explores the robustness of LASSO regression in predicting air pollution dynamics. The analysis establishes correlations and identifies key predictors, shedding light on the intricate relationships within the data. The paper contributes valuable insights to the field of air quality prediction, showcasing the efficacy of LASSO modeling in providing accurate and reliable forecasts, thus facilitating proactive measures for pollution mitigation and environmental management. Additionally, the aim of the paper is to investigate whether the COVID-19 pandemic exerted any discernible impact on pollution levels. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparative Analysis of Different Heliostat Field Control Algorithms(Society for Electronics, Telecommunications, Automatics and Informatics of the Republic of Macedonia - ETAI, 2021-09); This study presents the use of various algorithms for control of a field of heliostats, through which a thermal power plant with concentrated solar energy is controlled. The design of the control algorithms consists of several steps. First, in order to obtain the mathematical model of the system, the real system is identified according to the gray box and the least-square method. The data used to identify the system is generated by step excitation on the real system, for a specific sampling period. The resulting mathematical model is used to design and simulate a continuous and discrete PID controller, Mamdani and Sugeno fuzzy logic controllers, as well as ANFIS based fuzzy logic controller. The results of the applied controllers are analyzed and compared, based on the output overshoot, the rise and settling time. It can be concluded that we got best results (least settling time and the least overshoot) when fuzzy logic controller with ANFIS was used, while in terms of speed and rise time, the best results were obtained when discrete PID control algorithm was used. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Path Planning Using Fuzzy Logic Control of a 2-DOF Robotic Arm(IEEE, 2022-06-27) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Classification of Individual and Combined Finger Flexions Using Machine Learning Approaches(IEEE, 2022-06-27) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fuzzy Logic Based Algorithm for Electrical Load Management in Touring Buses(IEEE, 2023-06-27) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparing Classical Machine Learning and Deep Learning for Classification of Arrhythmia from ECG Signals(2023-11-30); Arrhythmia detection is a vital task for reducing the mortality rate of cardiovascular diseases. Electrocardiogram (ECG) is a simple and inexpensive tool that can provide valuable information about the heart’s electrical activity and detect arrhythmias. However, manual analysis of ECG signals can be time-consuming and prone to errors. Therefore, machine learning models have been proposed to automate the process and improve the accuracy and efficiency of arrhythmia detection. In this paper, we compare six machine learning models, namely ADA boosting, Gradient Boost, Random Forest, C-Support Vector (SVC), Convolutional Neural Network (CNN), and Long Short-Term Memory Network (LSTM), for arrhythmia detection using ECG data from the MIT-BIH Arrhythmia Database. We evaluate the performance of the models using various metrics, such as accuracy, precision, recall, and F1-score, on different classes of ECG beats. We also use confusion matrices to visualize the errors made by the models. We find that the CNN model is the best performing model overall, achieving accuracy of 95% and F1-score of 84.75%. SVC and LSTM were the second and third best, achieving accuracy of 94% and 93%, respectively. We also discuss the challenges of using ECG data for arrhythmia detection, such as noise, imbalance, and similarity of classes. We suggest some possible ways to overcome these challenges, such as using more advanced preprocessing and resampling techniques, or incorporating domain knowledge and expert feedback into the models. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Modeling the arrangement of turbines for onshore wind power plants under varying wind conditions(Wind Integration, 2018-10); ; ; ; Fickert, LotharWind field layout optimization concerning various parameters is a major point in planning and will influence the revenue for the whole life of the installation. Besides the obvious impact of wind distribution also other parameters like connection costs and levelized costs of energy influence the optimum layout and have to be included in a realistic optimization algorithm. In this paper the sophisticated optimization of wind field layout with of two fundamentally different heuristic algorithms is investigated. To do so, detailed real-world data from an existing wind field in Bogdanci, Macedonia is utilized by employing real wind field data we are able to calibrate model adequacy and ascertain a model that will serve as a referent guidance in the planning of future onshore wind fields. Different layouts were designed using sophisticated algorithms for handling the resulting high-dimensional, highly nonlinear optimization problem. In particular, a nondominated sorting genetic algorithm (NSGA) and a mixed discrete particle swarm optimization algorithm (MD-PSO) were applied. Both optimization algorithms established bi-objective fitness functions, in particular- minimizing the levelized cost of energy and maximizing the capacity factor. By comparing the results obtained with the existing layout, it is established that both optimization algorithms are adequate in determination of wind power plant layouts. It is proven that the implementation of sophisticated optimization methods can results in essential savings during the whole lifetime of the wind field. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Uncertainty Evaluation in Resource Assessment of Wind Energy Potential(WindEurope, 2019-06); The decision for exploitation wind for electrical energy production is based on assignment of its long-term mean speed and distribution parameters. This data is usually obtained from a measurement campaign with a short duration. Taking into account the real characteristics of the instrumentation, such as it is: accuracy, finite resolution, dynamic response, it is obvious that the measurements will provide information with appended uncertainty. Next, the wind speed obtained from a measurement campaign is supposed to be extrapolated in long-term period. The extrapolation possess its own uncertainty component as well. Additionally, uncertainty is present because of terrain's characteristics of the observed site. The overall uncertainty prescribed to a long-term wind speed can be easily transferred to the Weibull distribution scale and shape parameters, These uncertainties, on the other hand, lead to uncertainty existence in the calculated mean annual energy production. Several other factors affect the estimated energy production, such as turbine efficiency, and the characteristics provided by the manufacturer, especially the power curve. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Reducing Uncertainty in Wind Energy Resource Assessment by using Multivariable Distribution Model(WindEurope, 2019-06); ; Demerdziev, KirilHaving reliable and precise wind energy resource assessment is essential for further analysis for conversion of wind energy into electricity. Previous practices that are common for representing wind data by sector-wise Weibull distribution, over time have been replaced with different multivariate and multimodal wind distribution models which are far more precise. The paper presents an upgraded model for accurate characterization and predict the annual variation of wind conditions. It is proven that the assumption of a constant air density value, can lead to notable differences between the predicted and real wind power available at a given site. Therefore, along with the main wind parameters, speed and direction, air density treated as a variable in this paper. The method, based on the Multivariate Kernel Distribution model, is an improvement of the existing methods for representing the wind regimes. Before representing the multivariable wind distribution, a piecewise Bivariate probability density function is constructed. It is important to note that when modelling wind with sector-wise Weibull, we are assuming that the wind speed satisfies the same probability distribution inside a direction sector. Following, a Bivariate probability density function using piecewise joint distribution is carried out. Namely, this distribution contains all input parameters for calculating the multivariable Kernel distribution. For comparison of these two distributions, coefficients of determination are used. From Kernel's probability distribution function, the three parameters of the wind (speed, direction and air density) are further treated as continuous variables, which facilitates and refines all further steps for optimizing the distribution of wind turbines in one wind farm. Aside of the other advantages, this model provides information on strengths of wind speeds and the energy content in them, it also enables selection of the appropriate type of wind turbine design for deployment at a given location. The measured wind data used in this paper are from one existing wind farm and one wind measuring station with a good potential for further investigation. By this approach, we can calibrate model adequacy and ascertain a model that will serve as a referent guidance in the planning of future onshore wind farms. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Trends in the development of wind generation systems(MAKO CIGRE, 2019-10); The rapid technology development for utilization of wind energy, requires constant improvement of the mechanisms that make the conversion of this energy into electricity. For these reasons, it is necessary to continuously monitor the state and trends of the development of wind energy conversion systems. The paper provides an overview of the latest technological developments of multi megawatt wind generators. In addition, the technological and economic advantages and disadvantages of each wind energy conversion system are analyzed. At the same time, a comparative analysis of several types of wind generators has been made, based on: weight, material types, axial length of the generator, rotor diameter and energy yield. This comparison is appropriate for detecting and defining the adequate structure of a higher installed power generating system for both- onshore and offshore wind fields. In addition, the latest improvement and optimal techniques of wind generators, their operation in faulty conditions, the ways of networking, as well as their application to wind turbines with high installed power are shown. This review offers the opportunity to understand the procedures for future design of wind fields, consisting of multi-megawatt units for conversion of wind energy,