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Online power quality disturbance detection by support vector machine in smart meter 被引量:8
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作者 Imtiaz PARVEZ maryamossadat aghili +2 位作者 Arif I.SARWAT Shahinur RAHMAN Fahmida ALAM 《Journal of Modern Power Systems and Clean Energy》 SCIE EI CSCD 2019年第5期1328-1339,共12页
Power quality assessment is an important performance measurement in smart grids.Utility companies are interested in power quality monitoring even in the low level distribution side such as smart meters.Addressing this... Power quality assessment is an important performance measurement in smart grids.Utility companies are interested in power quality monitoring even in the low level distribution side such as smart meters.Addressing this issue,in this study,we propose segregation of the power disturbance from regular values using one-class support vector machine(OCSVM).To precisely detect the power disturbances of a voltage wave,some practical wavelet filters are applied.Considering the unlimited types of waveform abnormalities,OCSVM is picked as a semisupervised machine learning algorithm which needs to be trained solely on a relatively large sample of normal data.This model is able to automatically detect the existence of any types of disturbances in real time,even unknown types which are not available in the training time.In the case of existence,the disturbances are further classified into different types such as sag,swell,transients and unbalanced.Being light weighted and fast,the proposed technique can be integrated into smart grid devices such as smart meter in order to perform a real-time disturbance monitoring.The continuous monitoring of power quality in smart meters will give helpful insight for quality power transmission and management. 展开更多
关键词 MACHINE learning ONE-CLASS support VECTOR MACHINE Power quality Disturbances SMART grid SMART METER
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