Analysing the Electrical Fault Detection and Classification Capabilities of a Decision Tree Classifier

The identification of faults in traditional approaches often depends on intricate algorithms and considerable preparation of data. On the other hand, decision tree classifiers provide a more simple but effective method for automated fault identification and classification. The aim of this study is to evaluate how well a Decision Tree Classifier performs in the field of detecting and categorizing electrical faults. Electrical systems are vulnerable to a multitude of errors that have the potential to compromise the dependability and security of the whole infrastructure. The study utilises a dataset that consists of electrical signals obtained from various failure situations, such as short circuits, overloads, and ground faults. The information is used to train the Decision Tree Classifier, which aims to construct a prediction model for the purpose of recognising and categorising various forms of electrical failures. The research assesses the performance of the model by analysing important metrics like accuracy, precision, recall, and F1 score. The results indicate that the Decision Tree Classifier is capable of efficiently recognizing and classifying electrical defects, showcasing its adaptability in different fault scenarios. The results of this study provide significant contributions to the understanding of how decision tree classifiers may be used in the context of problem detection in electrical systems. These findings emphasise the efficacy of decision tree classifiers as a means of improving the dependability and robustness of power distribution networks. The study findings have significant implications for enhancing maintenance techniques and advancing the development of intelligent systems that provide real-time problem monitoring in electrical infrastructure.