As one of the most important functions of the power industry, power fault detection and anomaly identification are supported by power audit systems. In power audit systems, data mining algorithms are widely used to improve the identification accuracy as well as the stability of the system. This study examines the performance of the Decision Tree Algorithm, SVM Algorithm, and Bayesian Algorithm in determining the power fault in the power audit system. We used the dataset of power auditing system and divided it into training set and test set to build the classification model using Decision Tree Algorithm, SVM Algorithm and Bayesian Algorithm respectively and evaluated the test set to compare the three algorithms in terms of accuracy, precision and system stability. Experimental results show that the Bayesian Algorithm is outperformed by the Decision tree Algorithm and SVM Algorithm in the power audit system. The Decision tree Algorithm has better ability of feature processing and feature modeling. The Decision tree Algorithm can automatically select the best features for classification judgment.

Dazhong Wang, Yinghui Xu, Yongshuang Zhang, Peng Liu, Chuang Li, Baoliang Zhang. Application and Practice of Data Mining Algorithms in Power Evaluation System. International Journal of Big Data Intelligent Technology (2024), Vol. 5, Issue 1: 103-111. https://doi.org/10.38007/IJBDIT.2024.050112.

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