Research Article
Partial Discharge Type Detection utilizing Statistical Techniques (n-q) and Random Forest Method
@INPROCEEDINGS{10.4108/eai.16-5-2020.2303963, author={Priyanka Kothoke and Anupama Deshpande and Yogesh Chaudhari}, title={Partial Discharge Type Detection utilizing Statistical Techniques (n-q) and Random Forest Method}, proceedings={Proceedings of the First International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICASISET 2020, 16-17 May 2020, Chennai, India}, publisher={EAI}, proceedings_a={ICASISET}, year={2021}, month={1}, keywords={partial discharge phase-resolved statistical techniques random forest artificial neural network}, doi={10.4108/eai.16-5-2020.2303963} }- Priyanka Kothoke
Anupama Deshpande
Yogesh Chaudhari
Year: 2021
Partial Discharge Type Detection utilizing Statistical Techniques (n-q) and Random Forest Method
ICASISET
EAI
DOI: 10.4108/eai.16-5-2020.2303963
Abstract
Partial Discharge (PD) designs are critical instrument for the findings of high voltage (HV) protection frameworks. Human specialists can find conceivable protection absconds in different portrayals of the PD information. One of the most broadly utilized portrayals is Phase-Resove,d PD (PRPD) designs. So as to guarantee the dependable activity of H.V hardware, it is vit,al to rela,te the noticeable measurable attributes of P.Ds to t,he prope,rties of the imperfection and at last to decide the kind of the deformity. In present work, we have obtained and analyzed PRPD pattern (n-q) using statistical parameters such as mean, standard deviation, variance, skew-ness and kurtosis to detect type of PD & we have verified the obtained results by providing obtained statistical para-meters as an in-put for training of Artificial Neural Net-work (ANN) in Google colaboratory using Python for Random Forest Method to detect type of discharge such as either void, surface or corona.
