Partial Discharge Localization through k-NN and SVM

Permit Mathuhu Sekatane () and Pitshou Bokoro
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Permit Mathuhu Sekatane: Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Johannesburg 2028, South Africa
Pitshou Bokoro: Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Johannesburg 2028, South Africa

Energies, 2023, vol. 16, issue 21, 1-16

Abstract: Power transformers are essential for the distribution and transmission of electricity, but they are prone to degradation due to faults early on. Partial Discharge (PD) is the most significant pointer of insulation breakdown in high-voltage apparatus. Dissolved Gas Analysis (DGA) is a commonly used technique for detecting and diagnosing PD. However, DGA data often contain missing values, which can significantly affect the accuracy of PD diagnosis. To mitigate the issues of missing values, this paper proposes using the k-Nearest Neighbors (kNN) technique to impute the missing values in the dataset. Further, it combines kNN with a Support Vector Machine (SVM) to detect the possibility of a PD source in the high-voltage apparatus. The approach was evaluated on a real-world DGA dataset and achieved high classification performance and discriminatory power for distinguishing between PD and non-PD instances. The effectiveness of the missing value imputation technique was evaluated, and the proposed approach demonstrated improved accuracy and precision compared to methods without imputation. The proposed approach offers a current solution for PD analysis in power transformers using DGA data with missing values.

Keywords: machine learning; support vector machine; K-nearest network; dissolve gas analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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