Kanyanach Ritthanont, Natin Janjamraj, P. Apiratikul, K. Bhumkittipich
{"title":"Identification of Power Quality Disturbances in DG Integrated Power System based on Deep Learning Approach","authors":"Kanyanach Ritthanont, Natin Janjamraj, P. Apiratikul, K. Bhumkittipich","doi":"10.1109/ICPEI55293.2022.9987085","DOIUrl":null,"url":null,"abstract":"Nowadays, power distribution systems are increasingly integrated with different loads, and distributed generators cause power quality (PQ) disturbances. Therefore, the implementation of deep learning is one of the advanced technologies following the trends of energy 4.0 for the classification and identification of power quality disturbances for smart energy monitoring. This paper presents the methodology to identify voltage sag, voltage swell, and voltage interruption according to the IEEE 1159 proposed DG integrated power system. The simulation results showed that the accuracies of proposed identification have better performance than that of the conventional neural network.","PeriodicalId":223538,"journal":{"name":"2022 International Conference on Power, Energy and Innovations (ICPEI)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Power, Energy and Innovations (ICPEI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPEI55293.2022.9987085","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Nowadays, power distribution systems are increasingly integrated with different loads, and distributed generators cause power quality (PQ) disturbances. Therefore, the implementation of deep learning is one of the advanced technologies following the trends of energy 4.0 for the classification and identification of power quality disturbances for smart energy monitoring. This paper presents the methodology to identify voltage sag, voltage swell, and voltage interruption according to the IEEE 1159 proposed DG integrated power system. The simulation results showed that the accuracies of proposed identification have better performance than that of the conventional neural network.
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