Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12188/30790
Title: | Classifying Power Quality Disturbances in Noisy Conditions using Machine Learning | Authors: | Velichkovska, Bojana Markovska, Marija Gjoreski, Hristijan Tashkovski, Dimitar |
Keywords: | Machine Learning Feature extraction Classification Power Quality |
Issue Date: | Oct-2019 | Publisher: | The Jozhef Stefan Institute | Conference: | 22nd International Multiconference Information Society 2019 | Abstract: | When 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. | URI: | http://hdl.handle.net/20.500.12188/30790 |
Appears in Collections: | Faculty of Electrical Engineering and Information Technologies: Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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01_Classifying Power Quality Disturbances in Noisy Conditions using Machine Learning.pdf | When 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. | 162.97 kB | Adobe PDF | View/Open |
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