Utilization of Stockwell Transform and Random Forest Algorithm for Efficient Detection and Classification of Power Quality Disturbances

<table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td class="align_left"> </td><td class="align_center" colspan="2">Prediction</td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td class="align_left" rowspan="2">Truth</td><td class="align_center">TP</td><td class="align_center">FN</td></tr><tr><td class="align_center">FP</td><td class="align_center">TN</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr><tr class="table-fn"><td><div><sup>#</sup>Table <a href="../tab4/">4</a> is reproduced from the study by Ravi and Kumar 2023 [<a href="/journals/jece/2023/6615662/#B16" target="_blank">16</a>].<br/></div></td></tr></table>

<div>Confusion matrix for binary classification problem<sup>#</sup>.</div>

Journal of Electrical and Computer Engineering

tab4

Table 4

Table 4: Utilization of Stockwell Transform and Random Forest Algorithm for Efficient Detection and Classification of Power Quality Disturbances 