MENOUER, LoubnaBENNAI, ZohraKheldoun, Aissa(supervisor)2025-05-052025-05-052024https://dspace.univ-boumerdes.dz/handle/123456789/1525670 p.Migration from conventional power system to smart power system paradigm is expected to solve many problems related to reliability and environments. However, new kinds of vulnerability, such as cyber attacks can affec tit sstability .These vulnerabilities pose a significan tris ka shacker sca nmanipulat eth eoperational network by injecting false data. Such malicious activities can go undetected for a prolonged period, leading to severe consequences. The impact can range from infrastructure damage, financia llosses ,an dt opotentia lfatalities In this project, machine learning techniques are investigated to identify these faults in addition to the classical faults. Using a publicly available dataset produced in Mississippi State University’s Oak Ridge National Laboratory, simulations are run on Kaggle. Results show that the Extra-Trees algorithm produced in average superior results, with an accuracy of 95.31% for binary classificatio nan d96.90 %fo rthree-class classification ,an dRandom-Fores talgorith mwit h92.28 %accurac yfo rmulti-class classification .Thereb youtperformin git scounterpar talgorithm si nterm so faccu- racy, precision, recall, and F1-score.enSmart gridMachine learning algorithmsCyber attacksThesis