Enhancing Power Grid  Reliability with Machine Learning Algorithms for Fault Detection and Classification

Mobassir, Md. Sadi; Arpi, Safoat Saima; Fahad, Farhan-Uz-Zaman

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dc.contributor.author	Mobassir, Md. Sadi
dc.contributor.author	Arpi, Safoat Saima
dc.contributor.author	Fahad, Farhan-Uz-Zaman
dc.date.accessioned	2025-02-27T09:32:03Z
dc.date.available	2025-02-27T09:32:03Z
dc.date.issued	2024-06-25
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Pereira, “Hybrid fault diagnosis algorithms for transmission lines,” Electrical Engineering, vol. 100, no. 3, pp. 1689–1699, Sep. 2018, doi: 10.1007/s00202-017-0647-7. [30] P. Rajesh, R. Kannan, J. Vishnupriyan, and B. Rajani, “Optimally detecting and classifying the transmission line fault in power system using hybrid technique,” ISA Trans, vol. 130, pp. 253–264, Nov. 2022, doi: 10.1016/j.isatra.2022.03.017. [31] M. H. Davis et al., “Case studies in outcome-based education,” Med Teach, vol. 29, no. 7, pp. 717–722, Jan. 2007, doi: 10.1080/01421590701691429. [32] M. M. Syeed, A. Shihavuddin, M. F. Uddin, M. Hasan, and R. H. Khan, “Outcome Based Education (OBE): Defining the Process and Practice for Engineering Education,” IEEE Access, vol. 10, pp. 119170–119192, 2022, doi: 10.1109/ACCESS.2022.3219477	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/2323
dc.description	Supervised by Md. Arefin Rabbi Emon, Lecturer, Department of Electrical and Electronic Engineering (EEE) Islamic University of Technology (IUT) Board Bazar, Gazipur, Bangladesh This thesis is submitted in partial fulfillment of the requirement for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2024	en_US
dc.description.abstract	In the complex and expansive networks of modern electric power systems, the occurrence of faults is an inevitable challenge that can significantly affect grid reliability and stability. This thesis presents a comprehensive study on the enhancement of power grid reliability through the application of machine learning algorithms for fault detection and classification. The primary focus is on the development and implementation of a hybrid model combining Long Short-Term Memory (LSTM) networks and Support Vector Machines (SVM) to accurately identify and classify various types of power system faults. The research begins with a detailed analysis of power system faults, including their causes, characteristics, and impacts on the stability of the grid. A significant portion of the study is dedicated to the classification of symmetrical and asymmetrical faults, with an emphasis on the most common and disruptive types such as line-to-ground and three-phase faults. The hybrid LSTM-SVM model is then introduced, highlighting its design, training, and validation processes. Empirical results demonstrate that the proposed model achieves high precision and recall rates across all fault types, with an overall accuracy of 96.7%. This high level of performance indicates the model's robustness and effectiveness in real-time fault detection and classification, making it a viable solution for practical deployment in power systems. Furthermore, the thesis integrates principles of Outcome-Based Education (OBE) to align the research with specific educational and professional outcomes. This approach ensures that the project not only addresses technical challenges but also enhances the competencies and skills of engineering students, preparing them for real-world applications and professional practices. The findings of this research contribute significantly to the field of electrical engineering by providing a robust methodology for improving power grid reliability. The successful application of machine learning techniques in fault detection and classification paves the way for further advancements in smart grid technologies and proactive fault management strategies.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.subject	ML, Fault, Transmission, Hybrid, Power	en_US
dc.title	Enhancing Power Grid Reliability with Machine Learning Algorithms for Fault Detection and Classification	en_US
dc.type	Thesis	en_US