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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63799
Volume:
10 |
5 |
May 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
MASTER OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY
MASTER OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY
The increasing complexity of modern power systems and the integration of renewable energy sources have introduced significant challenges in maintaining smart grid stability and ensuring reliable fault detection. Traditional monitoring techniques often struggle to process the massive volume of heterogeneous data generated by smart meters, phasor measurement units, and IoT-enabled devices. This paper proposes a big data–driven intelligent framework for smart grid stability analysis and fault prediction using advanced machine learning techniques. The proposed system leverages real-time data analytics, feature optimization, and predictive modeling to identify unstable grid conditions and detect potential faults at an early stage. Experimental evaluation demonstrates improved prediction accuracy, reduced detection latency, and enhanced scalability compared to conventional approaches. By enabling proactive maintenance, automated monitoring, and data-driven decision support, the proposed framework enhances grid reliability, operational efficiency, and resilience, making it suitable for next-generation smart energy management systems. The proposed system integrates data preprocessing, feature engineering, and predictive modeling to analyze grid behaviour and identify abnormal patterns before critical failures occur. To address these limitations, this paper presents a big data–driven intelligent framework for smart grid stability assessment and fault prediction using advanced machine learning and data analytics techniques.
KEYWORDS
The project “Smart Grid Stability and Fault Prediction Using Big Data” has been successfully developed to address the challenges of ensuring reliability stability and efficiency in modern power systems. With the increasing complexity of power grids and the growing integration of renewable energy sources such as solar and wind maintaining proper grid balance and stability has become more challenging. This project demonstrates how algorithms and real-time monitoring can predict potential faults in advance and enhance overall system performance. Data collected from sensors smart meters and grid components is processed and analyzed using predictive analytical models to identify early signs of irregular behavior such as voltage oscillations frequency deviations and load imbalances. Early detection enables proactive fault management allowing operators to take preventive actions before actual failures occur. power outages and improves the grid. The integration of data visualization dashboards and alert mechanisms further supports grid managers in making well-informed and timely decisions. The proposed system grid monitoring methods and next-generation predictive analytics solutions. Experimental and testing results validate the effectiveness of Big Data in fault detection accuracy and reducing response time. In conclusion this project presents an innovative and practical solution for enhancing smart grid operations. By enabling predictive maintenance and intelligent fault management it contributes to the development of a sustainable efficient and reliable energy system. Looking ahead the system can be further enhanced by models real-time cloud computing and blockchain technology for secure energy tracking. These enhancements would improve system intelligence security transparency and future readiness of smart grid infrastructures.
1. Ghosh A. De D. & Mukherjee A. (2021). Stabilityand Fault Management. IEEE Transactions on Smart Grid 12(3) 2458–2470. 2. Kaur S. & Sharma R. (2021). Machine Learning-Based Fault Prediction in Smart Grids Using Big Data. International Journal of Electrical Power & Energy Systems 132 107–118. 3. Chen M. Zhang Y. & Qiu T. (2022). Smart Grid Monitoring and Fault Detection Using IoT and Big Data Technologies. 4. Kumar P. (2022). Real-Time Energy Data Analytics and Predictive Maintenance in Smart Grids. Journal of Energy Informatics 6(2) 145–159. 5. Singh A. & Patel N. (2022). Grid Operations and Fault Detection. IEEE Access 10 65487– 65498. 6. Gupta R. (2023).Applications Data Analytics in Smart Energy Systems. Elsevier Publications. 7. IEEE Smart Grid Research Articles. Retrieved from www.ieee.org 8. ScienceDirect Energy and power systems journals. Retrieved from https://www.sciencedirect.com 9. Apache Spark Documentation. Retrieved from https://spark.apache.org 10. Hadoop Distributed File System (HDFS) Documentation. Retrieved from https://hadoop.apache.org
Publication Date
5/22/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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