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@article{198982,
        author = {Rajneesh and Poonam Rani and Rajni},
        title = {A machine learning based intelligent framework for real time fault predication in electric power system},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {14632-14644},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198982},
        abstract = {Electric power systems are becoming increasingly complex due to rising demand, renewable integration, and rapidly changing grid dynamics, making real-time fault prediction critical for ensuring system stability and preventing widespread outages. Traditional protection and monitoring approaches often struggle with noisy, high-dimensional data and delayed detection, highlighting the need for intelligent, data-driven solutions. This study proposes a machine-learning-based framework that enables accurate and timely fault prediction using SCADA and PMU measurements. The methodology involves structured preprocessing—including data cleaning, noise filtering, normalization, feature extraction, and dimensionality reduction—applied to the Electric Power System Fault Prediction Dataset consisting of 4,800 labeled samples across major fault categories. Three models were developed and evaluated: Support Vector Machine (SVM), Random Forest, and a Hybrid SVM–Random Forest model. Performance evaluation using accuracy, precision, recall, F1-score, confusion matrices, and ROC curves shows that the hybrid model delivers the best results. Achieving 97.8% accuracy, 98.0% precision, 97.0% recall, and a 97.4% F1-score, the hybrid model demonstrates strong generalization and reliability for real-time fault prediction in modern electric power systems.},
        keywords = {Real-time fault prediction, machine learning, SCADA, PMU, SVM, Random Forest.},
        month = {April},
        }