International Journal on Science and Technology

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Smart Substation Monitoring using Raspberry Pi with Machine Learning

Author(s) Mr. Chemalla Rajkumar, Mr. Rajinikanth P, Mr. Bottupalli Sharath, Mr. Kollur Chandrajay
Country India
Abstract The project titled “Raspberry Pi-Based Intelligent Substation Monitoring System Using Machine Learning” focuses on the design and implementation of a smart monitoring and protection system for electrical substations. The primary objective of this project is to ensure safe, reliable, and automated operation of substation equipment by continuously monitoring critical electrical parameters and detecting abnormal conditions using Machine Learning techniques. The methodology adopted in this work involves the use of various sensors to measure key parameters such as voltage, current, frequency, and temperature in real time. These sensor readings are interfaced with a Raspberry Pi, which acts as the central processing unit. The collected data is compared with predefined reference values stored in a dataset. Machine Learning algorithms are employed to analyze the data patterns and identify deviations from normal operating conditions.In the event of any parameter exceeding safe threshold limits, the system detects the abnormality and generates specific alerts such as voltage alert, current alert, and temperature alert. A buzzer is activated to provide an immediate warning, and a relay mechanism is triggered to disconnect the load, thereby protecting the equipment from potential damage. For local monitoring, all system parameters and alerts are displayed on an LCD screen.An IoT module is integrated into the system to enable real-time remote monitoring. The processed data is uploaded to a cloud-based platform, allowing users to access system status through mobile or web applications. This ensures continuous supervision and improved control over substation operations.The novelty of the proposed system lies in the integration of Machine Learning-based fault detection, automated protection mechanisms, and IoT-enabled remote monitoring. The findings of this study indicate that the system effectively detects abnormal conditions, minimizes response time, enhances equipment safety, and reduces the need for manual inspection. It also demonstrates reliable real-time data transmission and supports predictive maintenance strategies. Therefore, the developed system provides an efficient, intelligent, and reliable solution for modern substation monitoring and protection, suitable for enhancing automation and operational safety in power systems.
Keywords Raspberry Pi, Substation Monitoring, Machine Learning, IoT, Real-Time Monitoring, Voltage Monitoring, Current Monitoring, Temperature Monitoring, Fault Detection, Relay Protection, Automation, Smart Grid, Remote Monitoring.
Field Engineering
Published In Volume 17, Issue 2, April-June 2026
Published On 2026-04-08
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