Simulation and Implementation of an Internet of Things (IoT)-Based Substation Automatic Monitoring and Control System
Samson Dauda Yusuf, Ibahwa Cyril Ehidiamen
Asian Journal of Research in Computer Science · pp. 1–17 · Published 9 Feb 2026
10.9734/ajrcos/2026/v19i2821Abstract
The Nigerian electricity sector is faced with serious challenges of power outages, an insufficiently regulated and controlled electricity supply and poor maintenance due to a lack of system automation and smart grid systems for real-time substation monitoring. In this study, an Internet of Things (IoT)-based Substation Automatic Monitoring and Control was simulated and implemented using STM32F103CBT6 and ATmega8l-8PU Microcontrollers. Message Queuing Telemetry Transport (MQTT) was used as the protocol for remote data communication, and the client interface and broker were chosen from cloud.shiftr.io. The phase ID, which includes voltage, current, frequency, power, and ambient temperature, was packed using a JavaScript Object Notation (JSON) data structure. The results show that the average voltage for the red phase was 209.2 V, the yellow phase was 211.8 V, and the blue phase was found to be 207.6 V for 963 sampled values. When a load was connected, the red phase's mean current and power were, respectively, 4.5 A and 946 W. The voltages were marginally below the 230 ± 6% volts, the recommended value by Nigerian Electricity Supply Industry (NESI) in 2015. The three phases' average frequency was 49 Hz, though a small decrease, but within the tolerance level. To avoid equipment loss and the risk to personnel safety, phase variables need to be routinely checked and automatically regulated when they surpass the preset values. This IoT-based system enhances the reliability and efficiency of substation operations through real-time data acquisition, analysis and control, demonstrating significant improvements in substation performance. The integration of MQTT-based communication, real-time monitoring, and automated control enhances data-driven decision-making in power infrastructure management and smart grid systems for predictive maintenance.
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