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Power Quality Disturbance Detection and Classification using Artificial Neural Network based Wavelet
| Content Provider | Semantic Scholar |
|---|---|
| Author | Bhagat, Amol S. Nimkar, Sagar D. Dongre, Kiran A. |
| Copyright Year | 2017 |
| Abstract | With an increasing usage of sensitive electronic equipment, power quality studies had grown to perform power quality data analysis. Wavelet transformation technique was founded to be more appropriate to analyze the various types of power quality events. This project compares the use of various types of wavelets at different scales and levels of decomposition on analyzing real recorded Power quality (PQ) events from transmission line model or signal generated using MATLAB background. Voltage sag, voltage swell and transient event have been tested. This method used to detect and classify power quality disturbance in the power system using Artificial Neural Network (ANN) and Wavelet transform. The proposed method requires less number of features as compared to conventional approach for the identification. The feature extracted through the wavelet is trained by Artificial Neural Network for the classification of events. After training the neural network, the weight obtained is used to classify the Power Quality (PQ) problems. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ripublication.com/ijcir17/ijcirv13n8_17.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
