Comparative Analysis of Partial Discharge Source Identification Using Machine Learning Method
Keywords:
Partial Discharge Source Identification, Machine Learning Methods, Artificial Neural Networks (ANN), Comparative Analysis, Convolutional Neural Networks (CNN)Abstract
The partial discharge (PD) in electrical systems is a very important part of making sure that the power grid is safe and reliable. In this study, we compare and contrast a wide range of machine learning techniques for finding causes of partial discharge. We specifically look at how well artificial neural networks (ANN), k-nearest neighbours (KNN), Gaussian Naive Bayes (GNB), and convolutional neural networks (CNN) can find different patterns connected with partial discharges. The study uses a large set of different electrical signals that were collected during PD events. These signals show a lot of different working conditions and discharge features. To rate the effectiveness of each machine learning method, we carefully look at its accuracy, precision, memory, and F1-score. Our results show what each method does well and what it can't do well, which helps us understand how well they work for different parts of finding partial discharge sources. The artificial neural network (ANN) shows that it can learn complex patterns and connections in data, making it a useful tool for finding the source of information. The K-nearest neighbours (KNN) method is good at finding local patterns, and the Gaussian Naive Bayes (GNB) method works best when statistical modelling is helpful. The convolutional neural network (CNN) is very good at finding spatial relationships in data. This is especially helpful when looking at sound patterns related to partial discharges. This paper helps to understand to find the source of a partial discharge, also gives students and practitioners who want to use machine learning in electrical power systems useful information. Findings help people make smart choices about which method to use based on their personal practical needs and the way partial discharge events happen.
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