Hameed,M.E.Cevik,M.2024-06-232024-06-2320220979-835033494-4https://doi.org/10.1109/ICCRESA57091.2022.10352083https://hdl.handle.net/20.500.12469/5809The increase in demand for energy increases the complexity of energy systems every day because of the increase in electricity consumption. Complex electrical energy systems consist of many equipment and parts from generation, transmission and distribution, and many power and generation systems do not increase at the same rate required for consumption and are vulnerable to damage in this paper, the major priority is on modern methods of detection, classification and analysis of many kinds of faults in electrical power systems, using machine learning algorithms, using Python and a library scikit-learn, which provides algorithms for supervised machine learning, data is processed using data science offices NumPy, pandas and matplotlib, and the detection and classification data is evaluated using 9 types of algorithms that are available in machine learning which are Support vector machine (SVMs), Logistics regression, Linear regression, Polynomial regression, Random Forest (RF), k-Nearest Neighbor (KNN), (MLP) Multilayer perceptron, Naive Bayes, Decision Tree, as well as all models they were compared and to be able to determine the best models which was created by the algorithms. © 2022 IEEE.eninfo:eu-repo/semantics/closedAccessDecision TreeFault Detection and ClassificationKNNLinear regressionLogistics regressionMulti-layer perceptronNaive BayesPolynomial regressionPower SystemsRandom ForestSupervised Machine LearningSVMConference Object36937410.1109/ICCRESA57091.2022.103520832-s2.0-85182602503N/AN/A