Faculty of Electrical Engineering and Information Technologies
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Item type:Publication, Classifying Power Quality Disturbances in Noisy Conditions using Machine Learning(The Jozhef Stefan Institute, 2019-10) ;Velichkovska, Bojana ;Markovska, Marija ;Gjoreski, HristijanWhen ensuring high-quality power supply of the power grid it is of the upmost importance to correctly detect and classify any power quality (PQ) disturbance. Selecting the most relevant features is very important in the process of training a genera machine learning model. Therefore, we analyze the power signals and extract information from them, and then select the most significant features. Additionally, an effective classification model is required. In this study we apply grid search throughout the features sets on one side, and the classification algorithms on the side. This way, we determine the most effective combination of an algorithm and feature set for classification of power quality disturbances. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, EVALUATING THE UNCERTAINTY OF A VIRTUAL POWER QUALITY DISTURBANCE GENERATOR(IMEKO, 2023) ;Velkovski, Bodan ;Markovska, Marija ;Kokolanski, Zivko; Taskovski, Dimitar - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Polyphase representation of QMF filter bank for power systems harmonics analysis(IEEE, 2015-09) ;Markovska, MarijaTaskovski, Dimitar - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Real-Time Implementation of Optimized Power Quality Events Classifier(Institute of Electrical and Electronics Engineers (IEEE), 2020) ;Markovska, Marija ;Taskovski, Dimitar ;Kokolanski, Zivko; Velkovski, Bodan - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Deep learning approach for classification of PQ disturbances(IEEE, 2022-06-28) ;Zlatkova, Aleksandra ;Markovska, MarijaTaskovski, Dimitar - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Optimal wavelet based feature extraction and classification of power quality disturbances using random forest(IEEE, 2017-07) ;Markovska, MarijaTaskovski, Dimitar - Some of the metrics are blocked by yourconsent settings
Item type:Publication, On the choice of wavelet based features in power quality disturbances classification(IEEE, 2017-06) ;Markovska, MarijaTaskovski, Dimitar - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Polyphase representation of QMF filter bank for power systems harmonics analysis(IEEE, 2015-09) ;Markovska, MarijaTaskovski, Dimitar - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Improving the Efficiency of Grounding System Analysis Using GPU Parallelization(ETAI - Society for Electronics, Telecommunications, Automatics and Informatics of the Republic of North Macedonia, 2021-09-21) ;Velkovski, Bodan; ;Gjorgievski, Vladimir ;Markovska, MarijaGrcev, LeonidSafety analyses of the effectiveness of large grounding systems are often hampered by the lengthy computation times. Using even the simplest image models, the evaluation of touch and step voltages can require from several minutes to several hours of computations on modern CPUs. Our analysis shows that substantial reduction of computation times can be achieved by utilizing GPU parallelization. In this paper we provide basic steps in the implementation of GPU parallelization on the simplest equipotential model for grounding analysis in homogeneous earth, and we test the effectiveness of this approach in different scenarios. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The effectiveness of wavelet based features on power quality disturbances classification in noisy environment(IEEE, 2018-05) ;Markovska, MarijaTaskovski, Dimitar
