Vishakha Pandya, S. Agarwal, Om Prakash Mahela, Sunita Choudhary
{"title":"Recognition of Power Quality Disturbances Using Hybrid Algorithm Based on Combined Features of Stockwell Transform and Hilbert Transform","authors":"Vishakha Pandya, S. Agarwal, Om Prakash Mahela, Sunita Choudhary","doi":"10.1109/SCEECS48394.2020.4","DOIUrl":null,"url":null,"abstract":"Research activity under taken in this article is concentrated on detection and classification of single stage power quality (PQ) disturbances. This has been achieved with the help of combined features of Stockwell transform, Hilbert Transform and decision supported rules. Proposed algorithm can be implemented in online PQ monitoring equipments. Performance of algorithm is evaluated for detection and classification of different PQ disturbances which include sag in voltage, swell in voltagel, momentary interruption (MI), oscillatory transient (OT), impulsive transient (IT), notch, spike and harmonics. This is established that performance of algorithm is better compared to Stockwell transform and ruled decision tree supported algorithm. Study is carried out using MATLAB software","PeriodicalId":167175,"journal":{"name":"2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCEECS48394.2020.4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Research activity under taken in this article is concentrated on detection and classification of single stage power quality (PQ) disturbances. This has been achieved with the help of combined features of Stockwell transform, Hilbert Transform and decision supported rules. Proposed algorithm can be implemented in online PQ monitoring equipments. Performance of algorithm is evaluated for detection and classification of different PQ disturbances which include sag in voltage, swell in voltagel, momentary interruption (MI), oscillatory transient (OT), impulsive transient (IT), notch, spike and harmonics. This is established that performance of algorithm is better compared to Stockwell transform and ruled decision tree supported algorithm. Study is carried out using MATLAB software