Construction and research of forecasting method of electrical fault based on machine learning and decision tree

Yousong Li

doi:10.1117/12.3039290

5 August 2024 Construction and research of forecasting method of electrical fault based on machine learning and decision tree

Yousong Li

Author Affiliations +

Proceedings Volume 13226, Third International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024); 132262L (2024) https://doi.org/10.1117/12.3039290
Event: 3rd International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024), 2024, Changsha, China

Abstract

Background: Machine learning is progressively utilized within the realm of electrical fault detection, enhancing the accuracy of fault discrimination. Objective: This paper aims to utilize five machine learning algorithms to predict faults in three-phase electrical power system and compare the predictive performance of five classical machine learning algorithms. Method: Logistic Regression, Decision Tree, Random Forest, XGBoost, and Support Vector Machine are employed to predict the existence and types of faults in three-phase electrical power system. The algorithms' performance is evaluated by comparing the predictive evaluation metrics. Results: In this paper, Decision Tree exhibited the optimal evaluation metrics, achieving an accuracy of 88% on the test set. Conclusion: The experimental results indicate that Decision Tree exhibits the best performance in predicting faults in power system. This study provides guidance and recommendations for decision-makers in relevant industries.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yousong Li "Construction and research of forecasting method of electrical fault based on machine learning and decision tree", Proc. SPIE 13226, Third International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024), 132262L (5 August 2024); https://doi.org/10.1117/12.3039290

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