MACHINE LEARNING APPROACH FOR SMART DISTRIBUTION TRANSFORMERS- LOAD MONITORING AND MANAGEMENT SYSTEM
SABARISAKTHI S
, SIBJEE KUMAR K , PONKARTHIGEYAN M , RAM KUMAR V , KARTHIKEYAN A
The rapid growth in electricity demand and the increasing complexity of
distribution networks have made conventional transformer monitoring systems
inadequate for ensuring reliable and efficient power delivery. This project presents a
machine learning-based smart distribution transformer system designed for real-time
load monitoring and intelligent load management. The proposed system integrates
advanced sensing, Internet of Things (IoT) technology, and data-driven predictive
models to enhance the operational efficiency and lifespan of distribution transformers.
The system continuously acquires critical parameters such as voltage, current, load,
oil temperature, and ambient conditions using embedded sensors. These parameters are
transmitted to a centralized platform through wireless communication modules,
enabling real-time data analysis and remote monitoring. The collected data is
preprocessed and used to train Machine Learning models capable of forecasting load
demand, detecting anomalies, and predicting potential faults before their occurrence.
Time-series forecasting techniques, such as Long Short-Term Memory (LSTM)
networks, are employed to predict future load patterns and identify peak demand
periods. Additionally, anomaly detection algorithms are utilized to identify abnormal
operating conditions, including overloading, overheating, and voltage fluctuations.
Classification models further assist in fault prediction, enabling proactive maintenance
and reducing the risk of unexpected transformer failures.
The proposed system also incorporates intelligent load management strategies, such
as dynamic load balancing and demand response mechanisms, to prevent overloading
and ensure optimal utilization of transformer capacity. Real-time alerts and notifications
are generated when critical thresholds are exceeded, allowing utility operators to take
timely corrective actions.
By leveraging machine learning and IoT technologies, this system significantly
improves the reliability, efficiency, and safety of power distribution networks. It
minimizes downtime, reduces maintenance costs, and supports the transition toward
smart grid infrastructure. The implementation of this approach demonstrates a scalable
and cost-effective solution for modern power systems, contributing to sustainable
energy management and improved service quality.
"MACHINE LEARNING APPROACH FOR SMART DISTRIBUTION TRANSFORMERS- LOAD MONITORING AND MANAGEMENT SYSTEM", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.11, Issue 5, page no.b661-b723, May-2026, Available :https://ijsdr.org/papers/IJSDRTH01023.pdf
Volume 11
Issue 5,
May-2026
Pages : b661-b723
Paper Reg. ID: IJSDR_309890
Published Paper Id: IJSDRTH01023
Downloads: 000318
Research Area: Science and Technology
Country: DINDIGUL, TAMILNADU, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
