ISSN2320-2882
Impact Factor9.17
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CrossRefDOI
ESTD2013
Peer ReviewTransparent
AccessOpen
Submission: Always Open
Paper Title
Fault Detection in Grid using Machine Learning and loT
Authors
Basavaraj N , Ullas U , Adarsh RK , Akash SB , Kiruthika K
Keywords
Fault deduction, power system, power grid, cyber security,
Abstract
The modern electrical grid demands high reliability,
stability, and real-time monitoring to prevent failures that can lead to blackouts, equipme damage, and economic loss. This project presents an loT-based smart grid fault detecti 2/7 system that collects real-time parameters such as voltage, current, temperature, and humidity using the ESP32 microcontroller. These readings are continuously transmitted to a Flask-based web application, which integrates a machine learning model trained to distinguish normal and faulty grid conditions. The model analyzes live sensor data to identify anomalies such as voltage drops, current deviations, overheating, or environmental
instability. The system provides instant visualization through a dashboard and generates alerts for preventive maintenance. This low-cost, scalable solution improves grid safety, enhances predictive fault identification, and supports real-time decision-making in smart power networks.
Keywords: Smart Grid, loT, ESP32, Machine Learning, Fault Detection, Anomaly Detection, Flask, Power Monitoring.
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How To Cite
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IJCRT — journal style
"Fault Detection in Grid using Machine Learning and loT", IJCRT - International Journal of Creative Research Thoughts (www.ijcrt.org), ISSN:2320-2882, Vol.13, Issue 12, page no.a321-a327, December 2025, Available :https://ijcrt.org/papers/IJCRT2512054.pdf
APA — 7th edition
N, B., U, U., RK, A., SB, A., & K, K. (2025). Fault Detection in Grid using Machine Learning and loT. International Journal of Creative Research Thoughts, 13(12), a321-a327. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512054
MLA — 9th edition
N, Basavaraj, et al. "Fault Detection in Grid using Machine Learning and loT." International Journal of Creative Research Thoughts, vol. 13, no. 12, 2025, pp. a321-a327, https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512054.
Chicago — 17th, bibliography
N, Basavaraj, et al. "Fault Detection in Grid using Machine Learning and loT." International Journal of Creative Research Thoughts 13, no. 12 (2025): a321-a327. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512054.
Harvard — author–date
N, B. et al. (2025) 'Fault Detection in Grid using Machine Learning and loT', International Journal of Creative Research Thoughts, 13(12), pp. a321-a327. Available at: https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512054
IEEE — numbered reference
B. N, U. U, A. RK, A. SB and K. K, "Fault Detection in Grid using Machine Learning and loT," IJCRT, vol. 13, no. 12, pp. a321-a327, Dec. 2025.
Vancouver — biomedical
N B, U U, RK A, SB A, K K. Fault Detection in Grid using Machine Learning and loT. IJCRT. 2025 Dec;13(12):a321-a327.
AMA — 11th edition
N B, U U, RK A, SB A, K K. Fault Detection in Grid using Machine Learning and loT. IJCRT. 2025;13(12):a321-a327. Accessed at https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512054
BibTeX — Zotero, Mendeley, LaTeX
@article{IJCRT2512054,
author = {Basavaraj N and Ullas U and Adarsh RK and Akash SB and Kiruthika K},
title = {Fault Detection in Grid using Machine Learning and loT},
journal = {International Journal of Creative Research Thoughts},
volume = {13},
number = {12},
pages = {a321-a327},
year = {2025},
month = {December},
issn = {2320-2882},
url = {https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512054}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - N, Basavaraj
AU - U, Ullas
AU - RK, Adarsh
AU - SB, Akash
AU - K, Kiruthika
TI - Fault Detection in Grid using Machine Learning and loT
T2 - International Journal of Creative Research Thoughts
JA - IJCRT
VL - 13
IS - 12
PY - 2025
SN - 2320-2882
UR - https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512054
SP - a321-a327
ER -
Issue
Volume 13 Issue 12,
December-2025
Pages : a321-a327
Article Information
- Published In
- International Journal of Creative Research Thoughts — Volume 13, Issue 12, December 2025
- Pages
- a321-a327
- Published
- December 2025
- Registration ID
- IJCRT_298086
- Published Paper ID
- IJCRT2512054
- Research Area
- Science and Technology
- Author Type
- Indian Author
- Location
- Bangalore Rural, Karnataka, India
- Peer Review
- Request Peer Review Report
- Licence
- CC BY 4.0 · Open Access
- ISSN & Impact Factor
- 2320-2882 · IF 9.17
- Downloads
- 264
- Permanent Link
- https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512054
- Published Paper PDF
- https://ijcrt.org/papers/IJCRT2512054.pdf
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International Journal of Creative Research Thoughts
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The authors would like to thank the reviewers and the editorial board of International Journal of Creative Research Thoughts for their careful reading and constructive comments, and all colleagues who supported the preparation of this manuscript.
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