Exploring AdaBoost’s Classifier’s Power in Electrical Fault Detection: A Comprehensive Analysis

Traditional methods rely on complex algorithms and extensive data preparation to detect errors. However, Adaboost classifiers offer an easier yet more efficient way to automatically detect and categorize faults. This study’s overarching goal is to assess an Adaboost Classifier’s efficacy in identifying and classifying electrical issues. A wide variety of mistakes can occur in electrical systems, jeopardizing the reliability and safety of the entire infrastructure. The research makes use of a dataset that includes electrical signals collected from a variety of failure scenarios, including overloads, ground faults, short circuits, and more. With this data, we can train the Adaboost Classifier to identify and classify different types of electrical failures using a prediction model. The study evaluates the model’s efficacy by examining crucial parameters such as F1 score, recall, accuracy, and precision. The results demonstrate that the Adaboost Classifier can adapt to various fault scenarios and efficiently recognize and classify electrical problems. Understanding the potential applications of Adaboost classifiers for fault detection in electrical systems is greatly enhanced by the findings of this study. To improve the reliability and resilience of power distribution networks, these results highlight the effectiveness of Adaboost classifiers. Improving maintenance practices and developing intelligent technologies to detect electrical infrastructure problems in real-time are both greatly affected by the study’s conclusions.